{
 "cells": [
  {
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    "tags": []
   },
   "source": [
    "# Membership\n",
    "An adversary can leverage a membership inference attack that takes advantage of query answers on a sequestered dataset to infer if a particular individual exists in the sequestered dataset.\n",
    "There are situations in which knowing if an individual exists within a sequestered dataset poses risk to the individual. \n",
    "\n",
    "The membership attacks in this notebook come from [DSSUV'15, Robust Traceability from Trace Amounts.](https://salil.seas.harvard.edu/publications/robust-traceability-trace-amounts)\n",
    "\n",
    "This notebook makes use of the Public Use Microdata Sample (PUMS), obtained from the Census Bureau’s American Community Survey (ACS).\n",
    "Attacks like the one demonstrated in this notebook motivate the use of statistical disclosure limitation techniques like differential privacy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e47065b3",
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    "cell_id": "00001-09bdd00a-74ff-41ff-967d-db4b57bd7dce",
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    "execution": {
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    "execution_millis": 22835515,
    "execution_start": 1645071625624,
    "source_hash": "6784914b"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "np.random.seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885d3e3d",
   "metadata": {
    "cell_id": "00002-5971bf9a-01d1-4b89-b1bb-e4f50bb2a0e8",
    "is_collapsed": false,
    "tags": []
   },
   "source": [
    "### Membership Attacks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0158b9",
   "metadata": {
    "cell_id": "00003-fe01f3c4-d2d1-499b-b711-b79d8aaece66",
    "tags": []
   },
   "source": [
    "An attacker wants to determine if Alice, an individual from the population, is a member of a sequestered dataset.\n",
    "Consider the sequestered dataset to be a sample from a broader population.\n",
    "\n",
    "It is assumed that the attacker is given the answers to a mean query mechanism applied to the sequestered dataset.\n",
    "The attacker collects the public information for Alice, as well as the public information for other individuals from the population (the reference sample). \n",
    "\n",
    "The gist of the attack is that if Alice is more similar to the query answers than the reference sample is to the query answers, then Alice is likely a member of the sequestered dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357f98be",
   "metadata": {
    "cell_id": "00004-13039230-d6b1-477b-b42f-7ac255b99724",
    "tags": []
   },
   "source": [
    "The first attack comes from figure 1, where the reference sample is assumed to be of size 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43616c37",
   "metadata": {
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "def membership_attack_fig_1(individual, answers, reference_samples, delta=.05):\n",
    "    \"\"\"Perform membership attack using dwork et al. test statistic.\n",
    "    See figure 1 in https://privacytools.seas.harvard.edu/files/privacytools/files/robust.pdf\n",
    "\n",
    "    :param individual: y, a boolean vector of shape (d,)\n",
    "    :param answers: q, a float vector with elements in [0, 1] of shape (d,)\n",
    "    :param reference_samples: z, a boolean vector of length (1, d)\n",
    "    :param delta: statistical significance\n",
    "    :return: True if alice is in data with (1-delta)100% confidence.\"\"\"\n",
    "    individual = individual * 2 - 1\n",
    "    answers = answers * 2 - 1\n",
    "    reference_samples = reference_samples * 2 - 1\n",
    "\n",
    "    alice_similarity = np.dot(individual, answers) # cosine similarity\n",
    "    reference_similarity = np.dot(reference_samples[0], answers)\n",
    "    statistic = alice_similarity - reference_similarity\n",
    "    \n",
    "    d = len(individual)\n",
    "    tau = np.sqrt(8 * d * np.log(1 / delta))\n",
    "    return statistic > tau"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0e0ff3",
   "metadata": {
    "cell_id": "00006-cc769172-4439-441e-be05-f5c65db121e8",
    "tags": []
   },
   "source": [
    "The second attack comes from figure 2, a generalization of figure 1 where the reference sample is larger. While the implementation is not as intuitive, it has greater power than figure 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c3a0a42",
   "metadata": {
    "cell_id": "00007-10897066-efde-4d1b-b4c1-185d91e5d2f1",
    "execution": {
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    "execution_start": 1645071625625,
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    "tags": []
   },
   "outputs": [],
   "source": [
    "def membership_attack_fig_2(individual, answers, reference_samples, delta=.05):\n",
    "    \"\"\"Perform membership attack using dwork et al. test statistic.\n",
    "    See figure 2 in https://privacytools.seas.harvard.edu/files/privacytools/files/robust.pdf\n",
    "\n",
    "    :param individual: y, a boolean vector of shape (d,)\n",
    "    :param answers: q, a float vector with elements in [0, 1] of shape (d,)\n",
    "    :param reference_samples: z, a boolean vector of length (*, d)\n",
    "    :param delta: statistical significance\n",
    "    :return: True if alice is in data with (1-delta)100% confidence.\"\"\"\n",
    "    individual = individual * 2 - 1\n",
    "    answers = answers * 2 - 1\n",
    "    reference_samples = reference_samples * 2 - 1\n",
    "\n",
    "    z, w = reference_samples[0], reference_samples[1:].mean(axis=0)\n",
    "    m, d = reference_samples[1:].shape\n",
    "    \n",
    "    alpha = np.sqrt(1 / m)\n",
    "    eta = 2 * alpha\n",
    "    \n",
    "    statistic = np.dot(individual - z, np.clip(answers - w, -eta, eta))\n",
    "    tau = 4 * alpha * np.sqrt(d * np.log(1 / delta))\n",
    "    \n",
    "    return statistic > tau"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd597c3",
   "metadata": {},
   "source": [
    "Both of these attacks have low power compared to a Monte Carlo simulation of the null distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d29a782",
   "metadata": {
    "cell_id": "00008-1b4f9ccc-19a1-423a-baf5-8221955ec532",
    "is_collapsed": false,
    "tags": []
   },
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141c1c0c",
   "metadata": {
    "cell_id": "00009-6dc66fc3-07a1-4eb7-a893-9052e8804d07",
    "tags": []
   },
   "source": [
    "In the following examples, we will consider the population to be 100 individuals from the PUMS dataset. For simplicity, we are assuming all columns are public. Since the tests expect boolean data, we also provide a function to project from a real dataset to a boolean predicate space. We'll be conducting our tests on the projected boolean space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4440fe85",
   "metadata": {
    "cell_id": "00010-414553f0-941d-4a40-97d8-ea1f99a1391c",
    "execution": {
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    },
    "execution_millis": 78,
    "execution_start": 1645071625626,
    "source_hash": "c265fd3b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "population: pd.DataFrame = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/opendp/cs208/main/spring2022/data/FultonPUMS5sample100.csv\")\n",
    "\n",
    "\n",
    "def make_boolean_projection(d_in, d_out=1):\n",
    "    \"\"\"Returns a (pseudo)random vector predicate function by hashing data.\"\"\"\n",
    "    prime = 691\n",
    "    desc = np.random.randint(prime, size=(d_in, d_out))\n",
    "    # this predicate maps data into a ndarray of booleans of size [n, d_out]\n",
    "    #   (where `@` is the dot product and `%` modulus)\n",
    "    return lambda data: ((data.values @ desc) % prime % 2).astype(bool)\n",
    "\n",
    "\n",
    "def draw(data: pd.DataFrame, sample_size: int):\n",
    "    \"\"\"Returns `sample_size` rows randomly from `data`, and the rest\"\"\"\n",
    "    indices = np.arange(len(data))\n",
    "    np.random.shuffle(indices)\n",
    "    return data.iloc[indices[:sample_size]], data.iloc[indices[sample_size:]]\n",
    "\n",
    "\n",
    "def execute_exact_query(data):\n",
    "    \"\"\"Computes the mean of each attribute.\"\"\"\n",
    "    return data.mean(axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ec15006",
   "metadata": {
    "cell_id": "00011-8d272b30-526f-44cc-93c6-1c39e2090362",
    "tags": []
   },
   "source": [
    "To set up this problem, we'll first create a sequestered dataset, sampled from the population.\n",
    "In practice, this sequestered dataset may be from, for example, a medical trial, or polls conducted by a political party. In those cases, the selection mechanism isn't a simple sample, and the strength of the attack may be increased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a25256b",
   "metadata": {
    "cell_id": "00012-4204f6de-8e3b-4e38-ace1-812937773acb",
    "execution": {
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     "shell.execute_reply": "2025-06-04T18:10:17.119014Z"
    },
    "execution_millis": 13,
    "execution_start": 1645071625709,
    "source_hash": "ad993e0b",
    "tags": []
   },
   "outputs": [],
   "source": [
    "sequestered_data, rest = draw(population, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc472de",
   "metadata": {
    "cell_id": "00013-7c7294c3-90b2-4b61-a0b9-d55f6c1ff2dc",
    "tags": []
   },
   "source": [
    "At this point the attacker takes over. \n",
    "The attacker builds a projection that maps data into a boolean space in which the tests can be conducted. \n",
    "He then collects the information he needs- public information for Alice, query answers on the sequestered data, and reference samples from the population. \n",
    "Keep in mind that the attacker does not need access to the sequestered data to retrieve the public information for Alice and the reference samples.\n",
    "\n",
    "If the membership attack evaluates to True, then the attacker can be 95% confident that Alice is a member of the sequestered dataset.\n",
    "If the membership attack evaluates to False, it is inconclusive if Alice is a member of the sequestered dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "508ebedb",
   "metadata": {
    "cell_id": "00014-f2c3d6f9-ab22-4d94-9e10-f873404dd37d",
    "execution": {
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     "iopub.status.idle": "2025-06-04T18:10:17.132627Z",
     "shell.execute_reply": "2025-06-04T18:10:17.132193Z"
    },
    "execution_millis": 446,
    "execution_start": 1645071625736,
    "source_hash": "2967fc1e",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.True_"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Projects the real-valued dataset into a boolean space with `d_out` columns\n",
    "project = make_boolean_projection(len(population.columns), d_out=4000)\n",
    "\n",
    "# Attacker computes the means of each of the attributes of the data in the projected space\n",
    "# An attacker can still execute this attack on a query interface via predicates\n",
    "answers = execute_exact_query(project(sequestered_data))\n",
    "\n",
    "# Attacker collects a reference sample from the population\n",
    "reference_samples = project(population.sample(1))\n",
    "\n",
    "# Attacker collects public info for Alice\n",
    "alice = project(sequestered_data.sample(1))[0]\n",
    "\n",
    "membership_attack_fig_1(\n",
    "    individual=alice, \n",
    "    answers=answers, \n",
    "    reference_samples=reference_samples, \n",
    "    delta=.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9ba61c",
   "metadata": {
    "cell_id": "00015-cdef5134-6191-45d0-b037-63cb72bf057e",
    "tags": []
   },
   "source": [
    "The following example is similar, but we expect the test not to pass, because Anita is not a member of the sequestered dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fb1cbeb",
   "metadata": {
    "cell_id": "00016-779cbaab-d3c1-4a50-96ec-97f0c909310c",
    "execution": {
     "iopub.execute_input": "2025-06-04T18:10:17.134834Z",
     "iopub.status.busy": "2025-06-04T18:10:17.134641Z",
     "iopub.status.idle": "2025-06-04T18:10:17.138603Z",
     "shell.execute_reply": "2025-06-04T18:10:17.138190Z"
    },
    "execution_millis": 369,
    "execution_start": 1645071625814,
    "source_hash": "ce47d81b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.False_"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attacker collects public info for Anita\n",
    "anita = project(rest.sample(1))[0]\n",
    "\n",
    "membership_attack_fig_1(\n",
    "    individual=anita, \n",
    "    answers=answers, \n",
    "    reference_samples=reference_samples, \n",
    "    delta=.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee23d3c5",
   "metadata": {
    "cell_id": "00017-fe9c10ee-8ecf-48c6-a030-ddb51c694878",
    "is_collapsed": false,
    "tags": []
   },
   "source": [
    "### Simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad33744d",
   "metadata": {
    "cell_id": "00018-0153c892-1724-4972-b5bd-9d120c7a1ec5",
    "tags": []
   },
   "source": [
    "In the following simulations, we will conduct the membership attack thousands of times on different sequestered datasets, random projections, choices of alice, and reference samples, to see how the attack performs as we vary the predicate space dimensionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78ccd5f4",
   "metadata": {
    "allow_embed": "code_output",
    "cell_id": "00019-90d2445b-8739-490e-a09d-f367c6632cd0",
    "execution": {
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    "execution_start": 1645071625851,
    "source_hash": "c557b4f1",
    "tags": []
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   "outputs": [],
   "source": [
    "def simulate_attacks(\n",
    "    query,\n",
    "    membership_attack, delta,\n",
    "    sample_size, reference_size, \n",
    "    num_predicates, num_trials):\n",
    "    \"\"\"Simulates `membership_attack` many times for different boolean space dimensionalities.\n",
    "\n",
    "    :param query: a function that maps data to query answers\n",
    "    :param membership_attack: either membership_attack_fig_1 or membership_attack_fig_2\n",
    "    :param delta: statistical significance of the result\n",
    "    :param sample_size: number of records to sample for the sequestered dataset\n",
    "    :param reference_size: number of referents to sample from the population\n",
    "    :param num_predicates: A vector of boolean space dimensionalities to test\n",
    "    :param num_trials: number of membership attacks to conduct for each boolean space dimensionality\n",
    "    :return: Tuple[np.ndarray, np.ndarray]; A vector false positive rates, and a vector of true positive rates\n",
    "    \"\"\"\n",
    "    fp_results = []\n",
    "    tp_results = []\n",
    "\n",
    "    for d in num_predicates:\n",
    "        fp = 0\n",
    "        tp = 0\n",
    "\n",
    "        print('Predicate space dimensionality:', d)\n",
    "\n",
    "        start_time = time.time()\n",
    "        for i in range(num_trials):\n",
    "            if i % (num_trials // 10) == 0:\n",
    "                print(f\"{i / num_trials:.0%} \", end=\"\")\n",
    "\n",
    "            # split the population into the sequestered data, and the rest\n",
    "            sequestered_data, rest = draw(population, sample_size)\n",
    "\n",
    "            # build new projection each trial (which contains new random predicates)\n",
    "            projection = make_boolean_projection(len(population.columns), d)\n",
    "\n",
    "            # answers to queries on the dataset to be attacked\n",
    "            answers = query(projection(sequestered_data))\n",
    "\n",
    "            # collect a reference sample from the population\n",
    "            reference_samples = projection(rest.sample(reference_size))\n",
    "\n",
    "            # collect an individual from the attacked dataset\n",
    "            alice = projection(sequestered_data.sample(1))[0]\n",
    "            tp += membership_attack(\n",
    "                individual=alice, answers=answers, \n",
    "                reference_samples=reference_samples, delta=delta)\n",
    "\n",
    "            # collect an individual from the population\n",
    "            anita = projection(rest.sample(1))[0]\n",
    "            fp += membership_attack(\n",
    "                individual=anita, answers=answers, \n",
    "                reference_samples=reference_samples, delta=delta)\n",
    "\n",
    "        print(f\"100% ~ {round(time.time() - start_time)} seconds (tp:{tp},fp:{fp})\")\n",
    "        fp_results.append(fp / num_trials)\n",
    "        tp_results.append(tp / num_trials)\n",
    "    return fp_results, tp_results\n",
    "\n",
    "\n",
    "def plot_fpr_tpr(num_predicates, fp_results, tp_results):\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.plot(num_predicates, fp_results)\n",
    "    plt.xlabel(\"Number of Predicates\")\n",
    "    plt.ylabel(\"False Positive Rate\")\n",
    "    plt.ylim(bottom=0., top=1.)\n",
    "    plt.title(\"False Positive Rate as Number of Predicates Increases\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(num_predicates, tp_results)\n",
    "    plt.xlabel(\"Number of Predicates\")\n",
    "    plt.ylabel(\"True Positive Rate\")\n",
    "    plt.ylim(bottom=0., top=1.)\n",
    "    plt.title(\"True Positive Rate as Number of Predicates Increases\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0741e3e",
   "metadata": {
    "cell_id": "00020-27305c70-20c6-472c-bf1b-df2fec2c23a5",
    "tags": []
   },
   "source": [
    "In this attack we use the membership attack from figure 1, and vary the predicate space to up to 1000 predicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71a5c80e",
   "metadata": {
    "cell_id": "00021-311b7c39-3f77-4ae2-bbc0-8b9935ec815c",
    "execution": {
     "iopub.execute_input": "2025-06-04T18:10:17.148645Z",
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     "shell.execute_reply": "2025-06-04T18:10:18.938079Z"
    },
    "execution_millis": 31228,
    "execution_start": 1645071625953,
    "source_hash": "9338756c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicate space dimensionality: 200\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% 90% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 0 seconds (tp:1,fp:0)\n",
      "Predicate space dimensionality: 400\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 0 seconds (tp:84,fp:0)\n",
      "Predicate space dimensionality: 600\n",
      "0% 10% 20% 30% 40% 50% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% 70% 80% 90% 100% ~ 0 seconds (tp:496,fp:0)\n",
      "Predicate space dimensionality: 800\n",
      "0% 10% 20% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% 40% 50% 60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 0 seconds (tp:897,fp:0)\n",
      "Predicate space dimensionality: 1000\n",
      "0% 10% 20% 30% 40% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:995,fp:0)\n",
      "FPR [np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)]\n",
      "TPR [np.float64(0.001), np.float64(0.084), np.float64(0.496), np.float64(0.897), np.float64(0.995)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_predicates = np.arange(1, 6) * len(population) * 2\n",
    "fp_results, tp_results = simulate_attacks(\n",
    "    execute_exact_query,\n",
    "    membership_attack_fig_1, \n",
    "    sample_size=5,\n",
    "    reference_size=1, \n",
    "    num_predicates=num_predicates, \n",
    "    num_trials=1000,\n",
    "    delta=0.05)\n",
    "print(\"FPR\", fp_results)\n",
    "print(\"TPR\", tp_results)\n",
    "\n",
    "plot_fpr_tpr(num_predicates, fp_results, tp_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6797bb17",
   "metadata": {
    "cell_id": "00022-700edba8-774c-41f8-93ca-dfc199645f38",
    "tags": []
   },
   "source": [
    "The test doesn't identify members outside of the sequestered dataset (false positive rate of zero), and becomes more powerful as the number of predicates increases (true positive rate increasing).\n",
    "\n",
    "These results look very significant, but keep in mind that the size of the sequestered dataset `sample_size` is only 5. Such a small sample size makes the query answers more distinctive, thus making it much easier to distinguish Alice."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3222f9c0",
   "metadata": {
    "cell_id": "00023-8acee24e-a19c-4cf1-8124-597ad700e5a0",
    "tags": []
   },
   "source": [
    "In this second simulation, we increase the sample size to a relatively large 30% of the population. In order to maintain power on the membership attack, we use the hypothesis test from figure 2 instead, collect a larger reference sample, and increase the number of predicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c0c5b83",
   "metadata": {
    "cell_id": "00024-37fa4f55-0704-45aa-af46-0c424b763549",
    "execution": {
     "iopub.execute_input": "2025-06-04T18:10:18.939685Z",
     "iopub.status.busy": "2025-06-04T18:10:18.939559Z",
     "iopub.status.idle": "2025-06-04T18:10:24.172228Z",
     "shell.execute_reply": "2025-06-04T18:10:24.171963Z"
    },
    "execution_millis": 550859,
    "execution_start": 1645071657206,
    "source_hash": "f395adb2",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicate space dimensionality: 200\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:5,fp:0)\n",
      "Predicate space dimensionality: 400\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:2,fp:0)\n",
      "Predicate space dimensionality: 600\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:14,fp:0)\n",
      "Predicate space dimensionality: 800\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:21,fp:0)\n",
      "Predicate space dimensionality: 1000\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 0 seconds (tp:29,fp:0)\n",
      "Predicate space dimensionality: 1200\n",
      "0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:37,fp:0)\n",
      "Predicate space dimensionality: 1400\n",
      "0% 10% 20% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:51,fp:0)\n",
      "Predicate space dimensionality: 1600\n",
      "0% 10% 20% 30% 40% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:63,fp:0)\n",
      "Predicate space dimensionality: 1800\n",
      "0% 10% 20% 30% 40% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:67,fp:0)\n",
      "Predicate space dimensionality: 2000\n",
      "0% 10% 20% 30% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:75,fp:0)\n",
      "Predicate space dimensionality: 2200\n",
      "0% 10% 20% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% 40% 50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:79,fp:0)\n",
      "Predicate space dimensionality: 2400\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% 20% 30% 40% 50% 60% 70% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% 90% 100% ~ 0 seconds (tp:86,fp:0)\n",
      "Predicate space dimensionality: 2600\n",
      "0% 10% 20% 30% 40% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% 60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 0 seconds (tp:90,fp:0)\n",
      "Predicate space dimensionality: 2800\n",
      "0% 10% 20% 30% 40% 50% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% 70% 80% 90% 100% ~ 0 seconds (tp:94,fp:0)\n",
      "Predicate space dimensionality: 3000\n",
      "0% 10% 20% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% 40% 50% 60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 0 seconds (tp:98,fp:0)\n",
      "Predicate space dimensionality: 3200\n",
      "0% 10% 20% 30% 40% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% 60% 70% 80% 90% 100% ~ 0 seconds (tp:97,fp:0)\n",
      "Predicate space dimensionality: 3400\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% 20% 30% 40% 50% 60% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% 80% 90% 100% ~ 0 seconds (tp:100,fp:0)\n",
      "Predicate space dimensionality: 3600\n",
      "0% 10% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% 30% 40% 50% 60% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% 80% 90% 100% ~ 0 seconds (tp:100,fp:0)\n",
      "Predicate space dimensionality: 3800\n",
      "0% 10% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% 30% 40% 50% 60% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% 80% 90% 100% ~ 0 seconds (tp:100,fp:0)\n",
      "Predicate space dimensionality: 4000\n",
      "0% 10% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% 30% 40% 50% 60% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% 80% 90% 100% ~ 0 seconds (tp:100,fp:0)\n",
      "FPR [np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)]\n",
      "TPR [np.float64(0.05), np.float64(0.02), np.float64(0.14), np.float64(0.21), np.float64(0.29), np.float64(0.37), np.float64(0.51), np.float64(0.63), np.float64(0.67), np.float64(0.75), np.float64(0.79), np.float64(0.86), np.float64(0.9), np.float64(0.94), np.float64(0.98), np.float64(0.97), np.float64(1.0), np.float64(1.0), np.float64(1.0), np.float64(1.0)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a more powerful attack with a much larger sample_size and more predicates\n",
    "num_predicates = np.arange(1, 21) * len(population) * 2\n",
    "fp_results, tp_results = simulate_attacks(\n",
    "    execute_exact_query,\n",
    "    membership_attack_fig_2, \n",
    "    sample_size=30,\n",
    "    reference_size=50, \n",
    "    num_predicates=num_predicates,\n",
    "    num_trials=100,\n",
    "    delta=0.05)\n",
    "\n",
    "print(\"FPR\", fp_results)\n",
    "print(\"TPR\", tp_results)\n",
    "plot_fpr_tpr(num_predicates, fp_results, tp_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed078c0a",
   "metadata": {
    "cell_id": "00025-7f270bab-7d17-4021-8e22-7694fcd855be",
    "tags": []
   },
   "source": [
    "The attack is more than effectively demonstrated at 4000 predicates, which gives an ~0% FPR and >80% TPR. It is trivial to increase the number of predicates or size of the reference sample to further increase the power of the attack.\n",
    "\n",
    "\n",
    "### Differential Privacy\n",
    "\n",
    "We now adjust our query interface to return answers protected by differential privacy. \n",
    "`execute_dp_query` releases an estimate of the mean for each predicate.\n",
    "The implementation splits the budget evenly over all input columns.\n",
    "By linear composition, the total privacy expenditure of the joint release of all means works out to be `epsilon`. \n",
    "As the number of attributes increases, the number of queries increases, which commensurately increases the noise scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e34b6c10",
   "metadata": {
    "cell_id": "acf8047a-3d36-44e4-8127-52aba3c9b957",
    "execution": {
     "iopub.execute_input": "2025-06-04T18:10:24.173618Z",
     "iopub.status.busy": "2025-06-04T18:10:24.173537Z",
     "iopub.status.idle": "2025-06-04T18:10:24.849923Z",
     "shell.execute_reply": "2025-06-04T18:10:24.849562Z"
    },
    "execution_millis": 36,
    "execution_start": 1645072207667,
    "source_hash": "66507eb9",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import opendp.prelude as dp\n",
    "from functools import lru_cache\n",
    "\n",
    "dp.enable_features(\"floating-point\", \"contrib\")\n",
    "\n",
    "epsilon = 1.\n",
    "\n",
    "@lru_cache(maxsize=None)\n",
    "def find_meas(size, num_queries):\n",
    "    epsilon_per = epsilon / num_queries\n",
    "    input_domain = dp.vector_domain(dp.atom_domain(bounds=(0., 1.), nan=False), size=size)\n",
    "    mean = dp.t.make_mean(input_domain, dp.symmetric_distance())\n",
    "    scale = dp.binary_search_param(\n",
    "        lambda s: mean >> dp.m.then_laplace(s, k=-40), \n",
    "        d_in=2, d_out=epsilon_per)\n",
    "    \n",
    "    return (input_domain, dp.l1_distance(T=float)) >> dp.m.then_laplace(scale, k=-40)\n",
    "\n",
    "\n",
    "def execute_dp_query(data):\n",
    "    \"\"\"Computes the dp means of `data`.\n",
    "    Assumes the number of rows is public information \n",
    "    The total privacy expenditure is `epsilon`.\"\"\"\n",
    "    exact_aggregates = data.mean(axis=0)\n",
    "    laplace = find_meas(*data.shape)\n",
    "    return np.array(laplace(exact_aggregates))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c1fb07",
   "metadata": {
    "cell_id": "926b221a-e49c-4cff-8ea9-7a254af996dc",
    "tags": []
   },
   "source": [
    "Run the simulation once more with this new query interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a25673eb",
   "metadata": {
    "cell_id": "a5c12d2f-5b5b-4689-a033-3c5225ceca6e",
    "execution": {
     "iopub.execute_input": "2025-06-04T18:10:24.851493Z",
     "iopub.status.busy": "2025-06-04T18:10:24.851342Z",
     "iopub.status.idle": "2025-06-04T18:11:01.931032Z",
     "shell.execute_reply": "2025-06-04T18:11:01.930804Z"
    },
    "execution_millis": 1725480,
    "execution_start": 1645072292842,
    "source_hash": "782bcbb1",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicate space dimensionality: 400\n",
      "0% 10% 20% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% 40% 50% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% 70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 1 seconds (tp:56,fp:47)\n",
      "Predicate space dimensionality: 800\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% 20% "
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    },
    {
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     "output_type": "stream",
     "text": [
      "30% 40% "
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    {
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      "50% 60% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% 80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 1 seconds (tp:47,fp:47)\n",
      "Predicate space dimensionality: 1200\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% "
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    },
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     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% 70% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% 100% ~ 2 seconds (tp:52,fp:54)\n",
      "Predicate space dimensionality: 1600\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% "
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    },
    {
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    {
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     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 3 seconds (tp:49,fp:51)\n",
      "Predicate space dimensionality: 2000\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 3 seconds (tp:52,fp:48)\n",
      "Predicate space dimensionality: 2400\n",
      "0% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% "
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    },
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    },
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     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "80% "
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    },
    {
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     "text": [
      "90% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 4 seconds (tp:56,fp:56)\n",
      "Predicate space dimensionality: 2800\n",
      "0% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% "
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    },
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    },
    {
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    },
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    {
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     "text": [
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    },
    {
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     "text": [
      "90% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 5 seconds (tp:49,fp:60)\n",
      "Predicate space dimensionality: 3200\n",
      "0% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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     "text": [
      "90% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 5 seconds (tp:54,fp:56)\n",
      "Predicate space dimensionality: 3600\n",
      "0% "
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "90% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 6 seconds (tp:54,fp:52)\n",
      "Predicate space dimensionality: 4000\n",
      "0% "
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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    {
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     "text": [
      "90% "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% ~ 7 seconds (tp:49,fp:46)\n",
      "FPR [np.float64(0.47), np.float64(0.47), np.float64(0.54), np.float64(0.51), np.float64(0.48), np.float64(0.56), np.float64(0.6), np.float64(0.56), np.float64(0.52), np.float64(0.46)]\n",
      "TPR [np.float64(0.56), np.float64(0.47), np.float64(0.52), np.float64(0.49), np.float64(0.52), np.float64(0.56), np.float64(0.49), np.float64(0.54), np.float64(0.54), np.float64(0.49)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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1rvpGCsa94q3M2De9fUTHuuqCaR3o/Kii3qOJtLzaVqo3F6ko3ls2j24cdR5S8BpaNyZHSBClmzwVnemGQUV4CmZVzKeA3zun6zdVlHi030zPd8UCgpsoUX0ZBTa622rdurU7QWtnVIbkaNGwTgiqzKbIWZkElekrUFJdCtXf0ef1XapPojukcLqD/DeBWaijVYD0+onQvH3xxRfujlDl8FoGvZbeeTkaHXCqY6E7DZ3wFVSoHNtrXu6tUwWBqZ3c03Oy04VVd0+iuy0FVSp/1gXdy2Ip8FHGRQGG6ntoGl18FMRqu6XnbkfTKLBRABtJaicgj+5oVUavitmqv6D1rO/UiT309zWdAmRl5LQvKQOlegPKgmWkWbGCIa1bBY6R+nFKbT/RukpNpH030muSVv2X1HjrQcusdRRJ+P4ZC61YdIx7+2Bawuc1vcsbXnk0K6iirY4F3ehoH9W+rm2r81h45fxIvGVRfaLUsknejZfqNuk7vfOlLsy60Ro+fHiK7GBGZOZ+GGmf0vLpHKLKvJEoUBPVk9LNrZZRXQIoM6mb1xEjRtjIkSNT/IayPcq+KKs+fvx4dwOomz+tEy2PflPZaN04R6IboPR+VywguIkSXfB1kQ1t6aHig/R2WqedUA/dMesipop1OlgVwatlgw4y3RF4B0Fmq1atmjsYFO17d6NeRUotg94PpSIRPRSA6KBT5TdlFVI7uWS01YiCG2UqlBJXlknpYQWKocGAgh5dTNNzYUgvVdhTdkoVTFXxT0GMKuGpAp7uTEMriUZqnZHacmobfvfdd267ZvSCqrvHiRMnuvWh7JEntbS8TohquaeH7s5UAVTbKaN9pmhf1EUrtFJl+J1t+P7tZfiyQvjy6pjQHb8XxGodi+60M3OfEO3/qigaTlkK7/3jLb3Lq2NF+1yk/SXSMkX6HWU1j3b+U0CsIDr0GFCl5fQcH15mURfy9Gw7VURX8bEeqvCsYECVfY81uEnPOtD5UcWPqQWSR/u85vNoy6bznbKZumkMzYSPGDHiiGmVVVGWRQ8FMDpnPfDAAy5I0e/oN+fNm+feP9r592jfFQtiJ4eUzSi6DY/y1ZomrTtZ0UVbZdOhFORoZ/PuuJQi1Pfr4hb+G3oeKYWfUSq+kPCu4r2o36ulrwtt+Dx4B3tad4hemj29wZ4CLK0H3UHooQt2aOsZrQ+lynUyiHTiVbHVsVJ2RutULYa835LQ5dbfkYprUltOZVS0L6jFSzht/7TWS6Tfj7St9P3hRWS6g1aLp2O5e9fJUdkbtWZav359ivd0QVXWK7SugOhuM6uo52jVxwi9oKpOkBe0qRhO86ymtLqQZOY+oeNDrX5mzJgRfE11W5TZUvPf9BRXZLb0Lq/2H2VDVG9ExaseFVnoIno0Os50kfRaTIby9slI+6jqfoWuL/FajYbv79pPVcStfS1SPa/QbRd+vlN2SlmdrMxQKSOlc7KKxMIztenJ7uj417qItL61LrxrgNajApHQ64aKXT/9u2WTR1nkcOHnYf2mssveeSyUiim1/6b3u2IBmZsoUVGG6iioOEonOu3IulP3mlmnRpXoVN6upn7Kymgn1/d4F2/RCUwZHDUb1I6uA01ZCzUl1glHlRrT2xtqWvVclHnSyVoHm7IXOpkrW6HfO+OMM9x0eq4LmCoXar50sdHBo4udFyCldiIW3Q0oA6M7NFUGTqvTNGVvlKnQnYzq3oSX/6o+iO4sVBdCTS613nWgquKd1n2kgzY9dLFUWb4Cu1tuucUVQ2lZtY51stCyKqiK1D+Nt5wqWtQFRdtRy6v1qUyQ0vQqS1fTUa0D3U2r4p8CJZVxR6LfU2Cn+jSqy6B6B0oXe/2geLQtVMSh79H21Elf60HdFKTWd8zRaHtpf9QdvuqChNJdsraB/ldxpwIdZbiyiu7WVaFXd+vKKCq400XN62xR+4eKKLT9NK+aTutK20z7idajilKPhYrm1JWBvlvbVvOiY0HbQPtCNOomZGR5dWOk4gYVWSqjp/OMbr70OdUFSYuKmRRI6hylImjt4zq2VFSs7LL2NZ3/lLXReUE3Qlovek/HZGjgpQySXtMNi853Wo861vRQnUBtX93UaJsqm6PtrHOpmpwrwBJ9XoGQ5kOfVzNwr/uDrKL9TMeCbk60DnXDqcyKji3dPOi4Pto61PrSelLxneZdwYWywpp3ndd1s6B1p/OOiptVj1GZV62XWrVqpdhOCrJ0vGl6ZQ01nc7LOv69Su+qh6Sm9Kosrf1BWWMFTco26nWvL570fFdMiHZzLb8KbSobiZo99u7dO1C6dOlA4cKFXfPMBQsWHNGsMrwp+LJly1wzxBNOOME11SxZsmTgjDPOcM22w3388ceBtm3buiaFetSrV881OV24cOG/mnfPgQMHAoMGDXLNSNUUu0qVKq5ZdmjTzNmzZ7vms1WrVg3ky5cvULZs2cB5550X+PXXX1N8V6TmnmoOqea8ap4Y2iw8fB2FNr/0mttPnTo14jxv2LDBrQPNq+a5fPnyrknpa6+9luayer+bWpPSt99+O0VTZzWb7dixo9u22sZ9+vQJzJs374jm0GoSe9tttwXKlCnjmjmHH5Kar2bNmrnmzkWKFHFNX++9997A2rVr05xXNddXM1Q1h1dz78suu8x9JnQ9qynuPffc45rK6ru1j+jvl19++ajrIq19RNtG74U2BfearKoZs+ZHv3f55Ze75sypNQUPb2qq79U8hgtvdu4dM2r6rv1R+5zWn7ZdaNNmj7pX6Nq1q2vKq31U21nzNnHixKPOU1rURP7SSy9120DHaosWLQJffvnlEdNltCn40aaN1IQ5o8srP/zwg9v38ubN65qVq0mwtx5CRToeN2/eHLj11lvd8avPqxm/pvG6YVBz6CeeeMJ9VvPQpEkTt240jV4LNX369OB8hO8rWsc9e/Z0x7GOZ/2ezi8fffRRcJrHHnvMrXttB+0HOg+qOww1mc7oekxtP/COh/CuK9566y23bFrGEiVKuH11woQJ6TqnqOsA7b+1atVyy67ziJq5P/PMMynm/c0333RN3/UbWjbNy8Cw7aRte+GFFwYqVqzovkv/67wc3tRc3/vkk0+648mbZ617nee3b9+eoe+Kthz6J9oBFgAAQGahzg0AAPAVghsAAOArBDcAAMBXohrcqMa1WsCo9rias4U3X4tEI96qHw5vlNRIowwDAIDsK6rBjZq2qVmgmq6lh5oLqvmZmhmreax691Wz0vT0vQAAALKHmGktpcyN+mBRHylpdZam7p5DO2FTnyDqZ0V9MgAAAMRVJ37qnCm8a2d1fBY+Gm4o9ZgY2muiNwKtOsvLaBf/AAAgOpSLUeejqspytI4w4yq4UZfuGjcolDeOkLqHjjQGj3qCVG+bAAAg/iUkJLgekX0T3BwLDUHgjdYsGktHw9Vr5ai7cQAAEPuUyNDo5BpO6GjiKrgpX768GzsklJ4rSElt5GS1qgodLdWjzxDcAAAQX9JTpSSu+rlp3bq1TZw4McVrEyZMcK8DAABEPbjR6K9q0q2H19Rbf69atSpYpNSzZ8/g9BqtdNmyZXbvvfe6kUo1EqlGK73rrruitgwAACC2RDW40dDzTZo0cQ9R3Rj9PWDAAPd83bp1wUBHatSo4ZqCK1uj/nGeffZZe+ONN1yLKQAAgJjq5+Z4VkgqVqyYq1hMnRsAAPx3/Y6rOjcAAABHQ3ADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK9EPbgZNmyYVa9e3fLnz28tW7a0mTNnpjn90KFDrW7dulagQAGrUqWK3XXXXbZv377jNr8AACC2RTW4GT16tPXt29cGDhxos2fPtkaNGlmXLl0sMTEx4vQjR460fv36uennz59vb775pvuO+++//7jPOwAAiE1RDW6ee+4569Onj/Xu3dvq169vw4cPt4IFC9pbb70Vcfrp06fbqaeealdeeaXL9nTu3Nm6d+9+1GwPAADIPqIW3Ozfv99mzZplHTt2/GdmcuZ0z2fMmBHxM23atHGf8YKZZcuW2bhx4+ycc85J9XeSkpJsx44dKR4AAMC/ckfrhzdt2mTJyclWrly5FK/r+YIFCyJ+Rhkbfa5t27YWCATs4MGDdtNNN6VZLDV48GAbNGhQps8/AACITVGvUJwRkydPtieeeMJefvllV0dn7Nix9tVXX9mjjz6a6mf69+9v27dvDz4SEhKO6zwDAIBskrkpXbq05cqVyzZs2JDidT0vX758xM889NBDdvXVV9v111/vnjds2NB2795tN9xwgz3wwAOuWCtcvnz53AMAAGQPUcvc5M2b15o1a2YTJ04Mvnbo0CH3vHXr1hE/s2fPniMCGAVIomIqAACAqGVuRM3Ae/XqZc2bN7cWLVq4PmyUiVHrKenZs6dVqlTJ1ZuR888/37WwatKkiesTZ8mSJS6bo9e9IAcAAGRvUQ1uunXrZhs3brQBAwbY+vXrrXHjxjZ+/PhgJeNVq1alyNQ8+OCDliNHDvf/mjVrrEyZMi6wefzxx6O4FAAAIJbkCGSz8hw1BS9WrJirXFy0aNFozw4AAMjk63dctZYCAAA4GoIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPCVYwpuDh48aN999529+uqrtnPnTvfa2rVrbdeuXZk9fwAAABmSO2OTm61cudLOOussW7VqlSUlJVmnTp2sSJEi9uSTT7rnw4cPz+hXAgAARC9zc8cdd1jz5s1t69atVqBAgeDrF198sU2cODHz5gwAAOB4ZG6mTJli06dPt7x586Z4vXr16rZmzZpjmQcAAIDoZW4OHTpkycnJR7y+evVqVzwFAAAQV8FN586dbejQocHnOXLkcBWJBw4caOecc05mzx8AAECG5AgEAoGMfEAZmi5dupg+tnjxYlf/Rv+XLl3afvzxRytbtqzFsh07dlixYsVs+/btVrRo0WjPDgAAyOTrd4aDG68p+OjRo23evHkua9O0aVPr0aNHigrGsYrgBgCA+JOlwY2yM23atLHcuXMfEfCoovFpp51msYzgBgCA+JOR63eG69ycccYZtmXLliNe14/pPQAAgGjKcHCjRI8qEYfbvHmzFSpUKLPmCwAAIGv7uenatav7X4HNNddcY/ny5Qu+p6bhv/32myuuAgAAiIvgRuVcXuZG/dmEVh5Wh36tWrWyPn36ZM1cAgAAZHZwM2LEiGBPxHfffTdFUAAAICYdU1PweEZrKQAA/H39zvDYUvLRRx/ZmDFj3Mjg+/fvT/He7Nmzj+UrAQAAotNa6n//+5/17t3bypUrZ3PmzLEWLVpYqVKlbNmyZXb22WdnzlwBAAAcr+Dm5Zdfttdee81efPFFV5H43nvvtQkTJtjtt9/uUkUAAABxFdyoKMpr8q0WUzt37nR/X3311fbBBx9k/hwCAABkZXBTvnz5YA/FVatWtZ9++sn9vXz5ctdMHAAAIK6CmzPPPNM+//xz97fq3tx1113WqVMn69atm1188cVZMY8AAABZ1xT80KFD7uENnDlq1Cg3YGbt2rXtxhtvdPVwYhlNwQEAiD9ZOnBmzpw5U4wIfsUVV7gWVLfddptt3LgxwzM7bNgw1zFg/vz5rWXLljZz5sw0p9+2bZvdcsstVqFCBTcERJ06dWzcuHEZ/l0AAOBPGQ5uIlm/fr0LbpS9yYjRo0db3759beDAga5/nEaNGlmXLl0sMTEx4vTqU0dFYCtWrHB97SxcuNBef/11q1SpUmYsBgAAyE7BzdatW6179+5WunRpq1ixosvWqHhqwIABVrNmTfvll1+CQzSk13PPPefGo1Ldnfr169vw4cOtYMGC9tZbb0WcXq+rMvOnn35qp556qsv4tG/f3gVFAICMSTqYHO1ZALJEunso7tevn6tboxHBv/nmG1eRePz48a6YatKkSW7gzIxQFmbWrFnWv3//4Gv6ro4dO9qMGTMifkYVmVu3bu2KpT777DMrU6aMXXnllXbfffdZrly5In4mKSnJPULL7AAgO9p/8JD9smKLTZyfaN8vTLTlm3Zb/QpFrcOJZe2MemWtUeXilitnjmjPJnD8gpuvv/7a3n77bdda6tZbb3XZmsaNG9sTTzxxTD+8adMmS05Odj0dh9LzBQsWRPyMekFWINWjRw9Xz2bJkiX2n//8xw4cOOCKtiIZPHiwDRo06JjmEQDi3cadSTZ5YaJNWpBoUxZvsl1JB1O8/9e6He7x4qQlVqpQXmtft4x1qFfO2tUpbUXz54nafAPHJbhZu3atnXjiie5vrwLwVVddZceTisHKli3rekhWpqZZs2a2Zs0ae/rpp1MNbpQZUr2e0MxNlSpVjuNcA8Dxowawf67d4bIzkxYm2ryEbSneL104n51Zr4ydWa+sNahYzGVyFPj8sGijbd6938bOXuMeuXPmsFOql3TTnXliWatZupDlyEFWBz4LbnTAhLaSUnChHoqPleru6Ds2bNiQ4nU9V0eBkaiFVJ48eVIUQSngUoVmFXNFaoauFlV6AIBf7U46aFOXbLLvFxzO0CTu/KcoXhpWKnY4SKlX1v2dM6ToqUrJgta1aWU7kHzIZq3c6j6vx5LEXTZj2Wb3eHzcfKtWqmDwO1rUKGn5ckeuCgDEXXDToUOHYICzd+9eO//8848IKNI7Krg+p8zLxIkT7aKLLgpmZvRcxV6RqBLxyJEj3XSqnyOLFi1yQU+s968DAJlp1eY9NmnBBpu4INF+XrbF9icfCr5XMG8ua1e7tAtEzqhb1soWzX/U78uTK6e1qlnKPe4/50RbuXl3MNDR96/cvMdGTFvhHoXy5rK2tUu74qvT65WxskWO/v1ATHbil956K6kVD6XWFLxXr1726quvutHFhw4damPGjHF1blT3pmfPnq6Zt+rNSEJCgjVo0MB9Rk3PFy9ebNdee60btPOBBx5I12/SiR+AeORlVpSdmfh3ZiVU1ZKHMyuqHJzZmRUvMzTp76Iu1eMJdXLlYi6I0m+fVDFlZgjILBm5fme4h+LM9tJLL7k6MypaUgVlNTFXZ35y+umnu/o9qsjsUUsqtdSaO3euC3yuu+66NFtLhSO4ARAvtuzebz8sSnT1Z35ctNF27PunMrDqxDSvXsJlT9TS6YQyx6dOzKFDh+v0HM7qbLB5q7eneL9MkXx2Rl3V6SnnsjuF86W7gADwT3BzvBHcAIhVOh0vWL8zWBw0e9VWCz1DlyyU106vU8ZV8G1Xu4wVKxD91kyJO/fZ5IUbXVZnyuKNtnv/P33n5MmVwxVzeVmdaqUKRXVeEd8IbtJAcAMgluzdn2zTl25ywYyKnNZu35fi/RPVD43qztQra42rxHY/NOpHZ+byLcGszorNe1K8X7NMoeCyqCWW6vkA6UVwkwaCGwDRtmbb3sMBwPwNNn3pZks6+E9l4Px5clrbWqVdAKCMR8Xix94qNdqWbdwVzEIp6Dl46J/LTZF8ue00ZaHqlbXT65axUoVp1Yq0EdykgeAGwPGWfChgc1b908xaRU+hKhUvEOxPpnXNUpY/j/+aWe/Yd8CmLt7k6g+pU0H1qeNRVSFlpc6se3gdqNdk+tRB1IKbffv2uc784gnBDYDjYfueA/bDYtVF2eA6yNu650DwPZUsNatWwlW6VVBTp1zhbHUxV6Xkeau3BYM9VVAOVb5ofpe5UhFWm1qlrGBeKiXDsja4UR8zjz/+uBvkUh3uqZ8ZDcXw0EMPuZZNar0UywhuAGQFnUrVPHvi3xdsNdtWxsajyr/t65RxFWtPq13GShSiby7P+u373FhXyupMW7LJ9h74p1Jy3tw5rc0JpYJ99qjTQWRPO7IyuHnkkUfsnXfecf9rRO8//vjDBTfqs0b91KQ26GWsILgBkFn2HUi2n5ZtDvY9s3rr3hTvKyPjZWeaVi1uualAm+516mV1WKc4LsFNrVq1XKd76q24SJEiNm/ePBfcqOM9jdi9detWi2UENwD+je17D9i439e5C6/qkJBlyDq6PC1O/LtS8vxEm7Uq9WyY/i9ekGyYn+3IwPU7wwWZGqhSAU6k4iqNzg0Afq0nMubXBHvqm4Wucz1PuaL5gpmEU6kfkqlUD6lOuSLucVP7E2zbHnVquNFlyiYv2mjb9hywz+etdQ+vHtPhujrlsl09JqSU4aOwfv36NmXKFKtWrVqK1z/66CNr0qRJRr8OAGKeWjo9/Pmfwd541V/LxY0r0bLnOFNm5sLGldzjYPIhm5uw7XAdp/mJtnDDTvtlxVb3eGr8wmzRAg2ZGNwMGDDAje2kDI6yNWPHjrWFCxfau+++a19++WVGvw4AYpbGUHpq/AL7cNbqYN8sd3Ssbb3aVKcDuihTXZvm1Uu6x31n1bPVW/cER0WftnSz60vovZ9Wukdo30EKeCoUi9++g5A+x9QUXJkbVShWfZtdu3ZZ06ZNXdDTuXNni3XUuQGQnkEq352x0oZOWGQ7kw6P53Rps8p271l1GQE7jnp9VlZHAc+6OO71Gf+gE780ENwASMv0JZts4Od/uoqs3ojXD1/QwJpWLRHtWcMx0CVu/jqN17XBZXXmJGw7crwuN9Bn7IzXhSgEN9dff71dddVVbsTueERwAyASFWM8/tVfNu739cGL3r1d6trlzatYTu7sfWPzriRXKVlZHY20vjMGRlpHDAQ3F154oX3zzTdWpkwZu+KKK6xHjx7WuHFjixcENwDC+1V57cdl9vLkJbbvwCHX6qZn6+p2V8c6Vqwgd/F+L378dcXWvzsQ3GBLN+5O8X61UgWDI5q3qFHS8uWmUrKvi6XUl82HH35oI0eOdPVv6tWr54KcK6+80vVSHMsIbrKHGUs3u4uVKoSqTF1FCk2qFrcTyhTmLhyOTn0T/tpgj371lyVsOdxRnC5ggy5o4OpkIPtZuXl3sPPAn5dtsf3J/wxoWihvLmtbu7TL6pxerwx1r/xe52b16tX2wQcf2FtvvWWLFy+2gwf/SfHFIoIbf5u9aqs9++1Cm7Zkc8T3i+bPbY0V6CjgqVbCBT6UsWc/SzfuskFf/OWKJbyxjO4/90Q7/+QKFEPA2ZV00HXS6FpgLUx0N0qhVBfLy+qcVLEYN01+Cm7Uad9XX31l//d//+f+L1mypGsiHssIbvzpjzXb7bkJi9wdl+TJlcOuOKWq6y1WfZMo6Plt9TZX7BCuVtnCrhv3w9mdEla7LNkdP1+wXpy42N6attwOJAcsb66cdn27GnbLGbWsUD4630PqHThqcM+JCza4YMfr78hTurA6cjxcKblt7TJWmH0pPoOb77//3hVJffzxx66vm65du7piqTPPPDPm73oIbvxl8Yad9vx3i4KVQNWk85Kmley2M2sf0fW9ytcXrt/pAp3ZK7e6VhMrN+854jvVl0njqsVddqdJtcNZHrp1j286zX02d609MW6+Jf59B64L0UPn1bcapQtFe/YQZxJ37rPJCzXie6JNWbzRdu//ZwgO3Vi1qlkqmNWpVor9Ky6Cm0qVKtmWLVvsrLPOcgHN+eefb/ny5bN4QXDjDys27bYXJi62T+eucc06FVNf0Kii3dGhttUsUzjd37NpV5LNXbXtcMCzaqvNS9ieYqwgj3qk9ert6H91B0/fGPHhz7XbXe/C6rnWqyQ64Lz61uHEctGeNfhA0sFk+2X5VpfVUeY4/IZJ5w6vT51Tqpek88dYDW5ef/11u+yyy6x48eIWjwhu4r+57kuTFtuYX1cHB9Dr0qCc3dWpjtUr/++3p7p0Vzfus1dtszl/Z3eWb0rZgsKrXNjo74rKTasVt8ZVSrimw4gdW3fvt2cnLLSRP68y7SoF8uSyW8+sZde1rUFX/MgSupwu27T78Cjx8xPtlxVb7GDIQJ/KCp9W53DxlfrWKVU4fhIDsYBO/NJAcBOfEnfss5cnL3UXKq8Fg04OfTvVsZMrZ22grUES5yaoKOtwhmdewrYUaWiPijeU2VG9HdXhqVuuiOsiHseXgt4PZq6yZ75d6AZWlPNOrmD3n3OiVSxOt/s4fnbsO2BTFm1yGZ3JCxNtc8iAq8o2q0GDl9VhjLIoBDeqU/P222+7L9PfadFYU7GM4Cb+7r6H/7DU3pmxIlgZuFXNknZ357puTJloXTwXuezOVpvzd5HWsrD+MaRg3lyuRYXL7vxdpMWdWtb6dcUW17uwKn+KAkz1Ltz6hFLRnjVkczpvzFu9LZjV+Wvd4X3UoxZ7CnIubVbJnS8IdP7d9TtdVbr1Zd6K1hey0nE87njemLLc3pq63LVwEQUHCmrUAiqa+6Dq2qgfFD16tKzmXtu2Z78rwlJRloq0NFqx5vunZVvcw6P6HqF1d+qWL0IZfCZl9gZ/vcA+mbMm2ORfWb2rWlUje4aYoPOGd6Pz3851bd32vfb9go2HB/pcssnW79jnMo56qMhbxadnn1Se88MxolgKMWV30kF7e/oK12Ps9r2HixSUrr27Sx3X+iBeAmvdpS1J3GVz/q6orIBHz8OpHkjDv7M7XsBTpgjZnfTaf/CQjZi23P43cbErKtTu0a15FbunS12yZIirXrJ/WrbZvvptnX02b63br6VisfxuBPorWlSlPy7L4jo3au6toqfwCsX60YsuusgmTZpksYzgJnYP7v/7aaW9MnlpsFxa/c/o7vusBuV90e+MgjVldLxm6Ap8Qse18VQpWcCaVDlcb0cdDSpDxN3bkTQ+0KAv/gwWCar+gnoX1l0vEK/UglPnwvdmrAyeC1XErTHOep9aPVs3Ld+RlcFNzpw5bf369Va2bNkUrycmJrpm4urYL5YR3MQW3aGM+TXBXpq0xKVlvaKbOzvWtgsaVfJ1c2t1DKaecr16O3poJOrwIzJf7pzBujtedqds0ezb9XvClj32yJd/uaETpHThvHbvWfXs0qaVfREEA94N3+dz19qbU5e7FpyizGTHE8vZ9W1ruKFC4iWTHdPBzW+//eb+1yCZys6oN2JPcnKyjR8/3l599VVbsWKFxTKCm9igJteqH6G+alZvPTyuT4Vi+e32DrXt0maVs22mQnWN1BrLC3j0v1c8F6pS8QLBQEfZHRXd5c3t73W2d3+yvTJ5iQ3/cZkLihX49mpd3e7sVNuK5idlD3/SJVrDybwxdZnrONBzUqWirl7OuQ0r+v7Yz9LgRhkbL0qM9JECBQrYiy++aNdee63FMoKb6Gcrvvp9netV2CtOUNflt55xgitXpv+RI9eX+s04XHfncFGWWmqFdJ3h6OTWsJKyO15T9BJWvpg/sjs634z/Y7099tV818+RqFK5WkGpM0Ugu1iSuNPenLrCxs5ebUl/18spVzSfG8W+R8uqvu9JfUdWBDcrV650J5maNWvazJkzrUyZMsH38ubN64qpcuWK/QsTwU10R2DW+E8L1h9OsRYvmMdubn+COzAL5I39fSdWqBXW4ezOPwHP1r/7cwmlTJhXlKWAR3d6+XLnirvhNR7+4s/gQKiqYPngefVdK5LslpIHQvveGvnzSntnxsrggJ758+R0We/ep9awEzLQS3s8oRO/NBDcHF/avaYs3uRG6vYGm1Mvnde3q2nXtq1uRShOyJR1vGLzHldR2SvKWrB+x5HZnVw5rUGloocrK1c7XKQVq53aqXjuhe8W2zvTV7geXpWZuum0mnbz6bUIhIGQoR++nLfO1csJ7TfnzHplXb0c9e/kp5uATA9uPv/8czv77LMtT5487u+0XHDBBRbLCG6On5+XbbZnv11kM1dsCTZ7Vm3/G06r6fv0aSw0qVeHYQp0vAyP7vbCqeMwr+6O/j+pUrGoFg2qGO7j2avtyfELXasR6VS/nD10bn2rWirlQKgADtNlXP1pvTl1mU1ckBhslFCvfBFXL+eCxhXjLmt7XIKb0BZS+jvVL8uRw1UujmUEN1lPzZ2VqVHGRnTXfVXLanbz6SfQh0uU6DBftWXP3yOib7M5CVtt/rqdwfG5Qkc0rl+xmBsJXRWVVYdHlZePx93fb6u3ud6FFZBJzdKFbMD59e30uilbZgJI3bKNu2zEtBX20azVwUGAVa+xZ+tqrl5OPPf/RLFUGghuss5fa3e4OjXfzT/cRDd3zhzW7ZQqbrDCCsVis/gjO9uz/6D9tnp7SMusrbZp15HZHQWkTYPZnRKuWXpmZnc270qyp79ZaKN/TXB3nBqUVK3mVHcgu7QCATLbtj377YOZCa5o1+tmQ91KdG1aya49tYbVjsPK+Mc9uNm2bVvcjBJOcJP51POuWj+pd01RVyNdm1a2OzrUtiolKUqIFzoVqFn+4ezO4Y4GFbCGjmrsBa31KxYNye6UsMolMp7dUXcA7/+8ymX5dvzdmeFFjSta/3NOtHLZuB8fIDMdSD5k434/XC9HNzMejU6uejntapeOm3o5WRrcPPnkk1a9enXr1q2be37ZZZfZxx9/bBUqVLBx48ZZo0aNLJYR3GSeVZv32NCJi+zTOWuClVc1+vJdner4trZ+dqO+ZX5fsz3FMBJe64xQSnuH1t1Rdqdg3tSHrlNX8w9//mew5Zz66Rl0YQM7JUqDoQJ+FwgE7NeVW+2NKcvs2782BOvl1ClX2GVyLmpSKea74sjS4KZGjRr2/vvvW5s2bWzChAl2+eWX2+jRo23MmDG2atUq+/bbby2WEdz8exrw7cVJS2zMLwnBu3pV+tRQCRoqAP6l04X6mlGQ8092Z7sdSA5EGFy0SLBllv5Xz9NKjz8xboF9MW9tsDsADSJ4ZYuqvu6NGoi1G9MR05e7c7jGZJOShfK6gWavblUtZutGZmlwo876Fi1aZFWqVLE77rjD9u3b53om1mstW7a0rVu3WiwjuDl2umN/efISV5TgDeym1KaCGo3rg+zbTfyfa7e7isreMBIbdhyZ3SlVKK/t2Z/sKjkqC66ARqO8lyhEyzkgWl0ujJ6Z4AYr9jrIVJcRFzauaNe1q2H1yheN2+t36nnjVJQoUcISEhJccKMhFx577DH3umKkWG8phWOvmPbqj8vs7WkrgrXvNa6JLkz6H9mbUtnNqpV0D89al9053OeO/v9zzY7gIIDNqpVwA1yq2TmA6CmaP4/1Oa2m66Jj/J/rXb0cHbMfzlrtHqfWKuWakp9eRy2l4yuzmuHgpmvXrnbllVda7dq1bfPmza7/G5kzZ47VqlUrK+YRUbJz3wG3s785ZbntTDpc4VMjLt/duY61rRU/ldBw/KlzQD3OO7lisLOxP9fusAMHD2XLAf+AWJY7V053rOoxa+VWe2vqcvv6j3WuZ3A9apYp5OrlXNK0ctx0opnhYimN+v3CCy+47M0111xjTZo0ca8///zzVqRIEbv++ustOxZLqfOx/cmHi2rincYs+WDmKhv+w1Lb9ne3/uoMSnUjOp5YlgsTAPjc6q17XDPyUTMTgje3qiOn4uRebapHpUUj/dxEIbhR6r3ry9PNbxSxq07NOSdViLu0JADg349lp4rHqoCcsGVvsLNPZXlUZHU8i5ezPLhZunSpDR061ObPn++e169f3+688043qGasI7hJn6olC7p+alSxTClLAED2lXxIgx8frpfzy4p/Gg61rFHSBTkdTiyX5S0eszS4+eabb9z4UY0bN7ZTTz3VvTZt2jSbN2+effHFF9apUyfLjsGNNrxX2dYP1EssxU8AgEhDpSjIUcetXncg6upB9XI0MnmhfBmuzhv94EZ1bLp06WJDhgxJ8Xq/fv1cHzezZ8+2WEZTcAAAMqfPs3emr7SRP68M9jJeNH9u6/53vRw1KshMWRrc5M+f337//XfXWiqU+rk5+eSTXb83sYzgBgCAzLM76aB9PHu1a2W1YvOeYK/lP/U/M1OrNWRpPzdlypSxuXPnHhHc6DWNGg4AALKPQvlyW8/W1e2qltVs0oJEe2PqMtefVTTra2Y4uOnTp4/dcMMNtmzZMjcEg1fnRmNO9e3bNyvmEQAAxLicOXNYx/rl3EP1UKMpw8VSmlwtpZ599llbu/bw+DAVK1a0e+65x26//faYr4RKsRQAAPEnS+vcJCUl2cGDB61QoUK2c+fhEX3VeV+8ILgBACD+ZOT6ne4CsY0bN7qhFgoXLuy+tFWrVpaYmBhXgQ0AAPC/dAc39913n6s0/Mgjj9gzzzxj27Zti/mhFgAAQPaT7grFEyZMsLffftv1cSPnnXeenXjiia6YKl++fFk5jwAAAJmfuVHl4UaNGgWfqym4gpp169al/9cAAACyWIYaoefKleuI59ls3E0AAOCXYikFMXXq1EnR1HvXrl1uOIacOf+JkbZs2ZL5cwkAAJDZwc2IESPSOykAAEDsBze9evXK2jkBAADIBNEb+AEAACALENwAAABfIbgBAAC+QnADAAB85ZiDm/3799vChQvdIJoAAABxG9zs2bPHrrvuOitYsKA1aNDAVq1a5V6/7bbbbMiQIVkxjwAAAFkX3PTv39/mzZtnkydPtvz58wdf79ixo40ePdqOxbBhw6x69eru+1q2bGkzZ85M1+dGjRrlOhW86KKLjul3AQCA/2Q4uPn000/tpZdesrZt26borVhZnKVLl2Z4BhQQ9e3b1wYOHGizZ89241dpcM7ExMQ0P7dixQq7++67rV27dhn+TQAA4F8ZDm42btxoZcuWPeL13bt3pwh20uu5556zPn36WO/eva1+/fo2fPhwV+T11ltvpfqZ5ORk69Gjhw0aNMhq1qyZ4d8EAAD+leHgpnnz5vbVV18Fn3sBzRtvvGGtW7fOcKXkWbNmuSKt4AzlzOmez5gxI9XPPfLIIy7AUt2fo0lKSrIdO3akeAAAAP9K9/ALnieeeMLOPvts++uvv1xLqRdeeMH9PX36dPvhhx8y9F2bNm1yWZhy5cqleF3PFyxYEPEzU6dOtTfffNPmzp2brt8YPHiwy/AAAIDsIcOZG9W1UWChwKZhw4b27bffuiyKMi3NmjWzrLRz5067+uqr7fXXX7fSpUunuwL09u3bg4+EhIQsnUcAABBnmRs54YQTXIDxbylAyZUrl23YsCHF63pevnz5I6ZXhWVVJD7//PODrx06dMj9nzt3btfvjuYtVL58+dwDAABkDxnO3KhF0++//x58/tlnn7mm2Pfff7+rQ5MRefPmddmeiRMnpghW9DxS/Z169eq531bmyHtccMEFdsYZZ7i/q1SpktHFAQAA2T24ufHGG23RokXu72XLllm3bt1c66YPP/zQ7r333gzPgJqBKwv0zjvv2Pz58+3mm292La/Uekp69uzpipZE/eCcdNJJKR7Fixe3IkWKuL8VLAEAgOwtw8VSCmwaN27s/lZA0759exs5cqRNmzbNrrjiChs6dGiGvk/BkZqXDxgwwNavX+++e/z48cFKxuoBWS2oAAAA0iNHIBAIWAYULVrUNd+uXbu2derUyc477zy74447XBBSt25d27t3r8UyNQUvVqyYq1ysZQEAALEvI9fvY+rn5rHHHrP33nvPNf0+99xz3evLly8/okk3AADA8Zbh4EbFTqpUfOutt9oDDzxgtWrVcq9/9NFH1qZNm6yYRwAAgKwrlkrNvn37XLPuPHnyWCyjWAoAgPiTkev3MfVzE0noCOEAAADRkq7gpkSJEukeFHPLli3/dp4AAACyNrjJaPNuAACAmA5uevXqlfVzAgAAkAly/9tKxOFDLlBJFwAAxFVTcA2NoGbgGgm8UKFCrj5O6AMAACCughuNHzVp0iR75ZVX3Gjbb7zxhg0aNMgqVqxo7777btbMJQAAQFYVS33xxRcuiDn99NPd4Jbt2rVzHflVq1bN3n//fevRo0dGvxIAACB6mRs19a5Zs2awfo3X9Ltt27b2448/Zt6cAQAAHI/gRoGNxpGSevXq2ZgxY4IZneLFix/LPAAAAEQvuFFR1Lx589zf/fr1s2HDhrneie+66y675557Mm/OAAAAsnJsqWXLllmNGjWO6Kl45cqVNmvWLFfv5uSTT7ZYx9hSAADEn4xcv9Odualdu7Zt3Lgx+Lxbt262YcMGV5G4a9eucRHYAAAA/0t3cBOe4Bk3bpzr8wYAACCu69wAAAD4IrhRXZvw+jbpHSkcAAAg5jrxU7HUNddc43ol9saVuummm9wQDKHGjh2b+XMJAACQ2cFN+MjgV111VXo/CgAAEHvBzYgRI7J2TgAAADIBFYoBAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK/ERHAzbNgwq169uuXPn99atmxpM2fOTHXa119/3dq1a2clSpRwj44dO6Y5PQAAyF6iHtyMHj3a+vbtawMHDrTZs2dbo0aNrEuXLpaYmBhx+smTJ1v37t3t+++/txkzZliVKlWsc+fOtmbNmuM+7wAAIPbkCAQCgWjOgDI1p5xyir300kvu+aFDh1zActttt1m/fv2O+vnk5GSXwdHne/bsedTpd+zYYcWKFbPt27db0aJFM2UZAABA1srI9TuqmZv9+/fbrFmzXNFScIZy5nTPlZVJjz179tiBAwesZMmSEd9PSkpyKyT0AQAA/Cuqwc2mTZtc5qVcuXIpXtfz9evXp+s77rvvPqtYsWKKACnU4MGDXaTnPZQVAgAA/hX1Ojf/xpAhQ2zUqFH2ySefuMrIkfTv39+lsLxHQkLCcZ9PAABw/OS2KCpdurTlypXLNmzYkOJ1PS9fvnyan33mmWdccPPdd9/ZySefnOp0+fLlcw8AAJA9RDVzkzdvXmvWrJlNnDgx+JoqFOt569atU/3cU089ZY8++qiNHz/emjdvfpzmFgAAxIOoZm5EzcB79erlgpQWLVrY0KFDbffu3da7d2/3vlpAVapUydWdkSeffNIGDBhgI0eOdH3jeHVzChcu7B4AACB7i3pw061bN9u4caMLWBSoNG7c2GVkvErGq1atci2oPK+88oprZXXppZem+B71k/Pwww8f9/kHAACxJer93Bxv9HMDAED8iZt+bgAAADIbwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AYAAPgKwQ0AAPAVghsAAOArBDcAAMBXCG4AAICvENwAAABfIbgBAAC+QnADAAB8heAGAAD4CsENAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAX4mJ4GbYsGFWvXp1y58/v7Vs2dJmzpyZ5vQffvih1atXz03fsGFDGzdu3HGbVwAAENuiHtyMHj3a+vbtawMHDrTZs2dbo0aNrEuXLpaYmBhx+unTp1v37t3tuuuuszlz5thFF13kHn/88cdxn3cAABB7cgQCgUA0Z0CZmlNOOcVeeukl9/zQoUNWpUoVu+2226xfv35HTN+tWzfbvXu3ffnll8HXWrVqZY0bN7bhw4cf9fd27NhhxYoVs+3bt1vRokUzeWkAAEBWyMj1O6qZm/3799usWbOsY8eO/8xQzpzu+YwZMyJ+Rq+HTi/K9KQ2PQAAyF5yR/PHN23aZMnJyVauXLkUr+v5ggULIn5m/fr1EafX65EkJSW5h0cRnxcBAgCA+OBdt9NT4BTV4OZ4GDx4sA0aNOiI11X0BQAA4svOnTtd8VTMBjelS5e2XLly2YYNG1K8rufly5eP+Bm9npHp+/fv7yose1SnZ8uWLVaqVCnLkSOHRTsKVZCVkJCQ7er/sOzZb9mz63Jn52XPrsstLHuVTF92ZWwU2FSsWPGo00Y1uMmbN681a9bMJk6c6Fo8ecGHnt96660RP9O6dWv3/p133hl8bcKECe71SPLly+ceoYoXL26xRBs/u+38HpY9+y17dl3u7Lzs2XW5hWUvmqnfebSMTcwUSymr0qtXL2vevLm1aNHChg4d6lpD9e7d273fs2dPq1SpkitekjvuuMPat29vzz77rJ177rk2atQo+/XXX+21116L8pIAAIBYEPXgRk27N27caAMGDHCVgtWke/z48cFKw6tWrXItqDxt2rSxkSNH2oMPPmj333+/1a5d2z799FM76aSTorgUAAAgVkQ9uBEVQaVWDDV58uQjXrvsssvcI96puEydF4YXm2UHLHv2W/bsutzZedmz63ILyz4wqsse9U78AAAAfDX8AgAAQGYiuAEAAL5CcAMAAHyF4AYAAPgKwU0me/jhh13Px6GPevXqBd/ft2+f3XLLLa6H5MKFC9sll1xyRI/Lav6uPnwKFixoZcuWtXvuuccOHjxosebHH3+0888/3/UWqeVUk/xQqquuJv4VKlSwAgUKuAFPFy9enGIa9Rbdo0cP19GTOle87rrrbNeuXSmm+e2336xdu3aWP39+1+vlU089ZbG+7Ndcc80R+8FZZ50V98uu/qZOOeUUK1KkiNs31fnmwoULU0yTWfu4Wko2bdrUtbioVauWvf322xbLy3366acfsc1vuummuF5ueeWVV+zkk08OdsimDlO//vprX2/v9C67X7d5uCFDhrhlC+08N+a3u1pLIfMMHDgw0KBBg8C6deuCj40bNwbfv+mmmwJVqlQJTJw4MfDrr78GWrVqFWjTpk3w/YMHDwZOOumkQMeOHQNz5swJjBs3LlC6dOlA//79A7FG8/bAAw8Exo4dqxZ3gU8++STF+0OGDAkUK1Ys8OmnnwbmzZsXuOCCCwI1atQI7N27NzjNWWedFWjUqFHgp59+CkyZMiVQq1atQPfu3YPvb9++PVCuXLlAjx49An/88Ufggw8+CBQoUCDw6quvBmJ52Xv16uWWLXQ/2LJlS4pp4nHZu3TpEhgxYoSbn7lz5wbOOeecQNWqVQO7du3K1H182bJlgYIFCwb69u0b+OuvvwIvvvhiIFeuXIHx48cHYnW527dvH+jTp0+Kba5tGM/LLZ9//nngq6++CixatCiwcOHCwP333x/IkyePWxd+3d7pXXa/bvNQM2fODFSvXj1w8sknB+64447g67G+3QlusiC40QUrkm3btrkD48MPPwy+Nn/+fHdxnDFjhnuuHSBnzpyB9evXB6d55ZVXAkWLFg0kJSUFYlX4Bf7QoUOB8uXLB55++ukUy58vXz53kRbtzPrcL7/8Epzm66+/DuTIkSOwZs0a9/zll18OlChRIsWy33fffYG6desGYkVqwc2FF16Y6mf8suyJiYluOX744YdM3cfvvfded5MQqlu3bi7IiMXl9i50oSf/cH5Ybo/2yzfeeCPbbO9Iy54dtvnOnTsDtWvXDkyYMCHFssbDdqdYKguo6EXFFTVr1nTFDkrNyaxZs+zAgQOueMajIquqVavajBkz3HP937Bhw2APzdKlSxc3ENmff/5p8WL58uWux+nQZdWYIC1btkyxrCqO0dAbHk2vHql//vnn4DSnnXaaG4csdH2oSGDr1q0Wy5RuVSq2bt26dvPNN9vmzZuD7/ll2bdv3+7+L1myZKbu45om9Du8abzviLXl9rz//vtuQGD1mK5Be/fs2RN8zw/LnZyc7Ia80RA5KqLJLts70rJnh21+yy23uGKl8PmLh+0eEz0U+4ku3ioz1AVt3bp1NmjQIFdn4o8//nAXe12owgfu1MbXe6L/Q3cG733vvXjhzWukZQldVl38Q+XOndtdMEKnqVGjxhHf4b1XokQJi0WqX9O1a1c370uXLnVDhZx99tnuoM2VK5cvll2D3KoM/tRTTw0Of5JZ+3hq0+jEuHfvXleHK5aWW6688kqrVq2au7FRXan77rvPBaJjx46N++X+/fff3QVd9SxUv+KTTz6x+vXr29y5c32/vVNbdr9v81GjRtns2bPtl19+OeK9eDjOCW4ymS5gHlVEU7CjnX/MmDFRPUBxfF1xxRXBv3X3on3hhBNOcNmcDh06mB/ork5B+9SpUy07SW25b7jhhhTbXBXpta0V3GrbxzPdrCmQUcbqo48+coMd//DDD5YdpLbsCnD8us0TEhLcINUTJkxwjRniEcVSWUyRbZ06dWzJkiVWvnx5279/v23bti3FNKphrvdE/4fXOPeee9PEA29eIy1L6LImJiameF816dWKyG/rQ0WUSl1rP/DDsmssuC+//NK+//57q1y5cvD1zNrHU5tGLVaieZOQ2nJHohsbCd3m8brcuktXS5ZmzZq5lmONGjWyF154wffbO61l9/M2nzVrljs/qRWTMsp6KKD73//+5/5WdiXWtzvBTRZT015F8YrodXDkyZPHJk6cGHxfKUzVyfHKcPW/0qChFz5Fz9rYXio0Hqg4RTtu6LIq1aj6JKHLqoNDB5Jn0qRJLu3vnSQ0jZpdq3w3dH3obiraxTIZsXr1alfnRvtBPC+76k/rAq/UvOY3vNgss/ZxTRP6Hd40oXUdYmm5I9HdvoRu83hb7tRoP01KSvLt9k7Psvt5m3fo0MHNt5bHe6h+oOqQen/H/Hb/11WSkcJ///vfwOTJkwPLly8PTJs2zTWDU/M3ta7wms+pCemkSZNc87nWrVu7R3jzuc6dO7smp2oSV6ZMmZhsCq6a9Grip4d2peeee879vXLlymBT8OLFiwc+++yzwG+//eZaD0VqCt6kSZPAzz//HJg6daqrmR/aHFq18tUc+uqrr3bNL0eNGuWaDka7KXhay6737r77btdqQPvBd999F2jatKlbtn379sX1st98882ueb/28dDmr3v27AlOkxn7uNdE9J577nGtMIYNGxbV5rFHW+4lS5YEHnnkEbe82uba52vWrBk47bTT4nq5pV+/fq5VmJZLx7Geq1Xft99+69vtnZ5l9/M2jyS8ZVisb3eCm0ymZmwVKlQI5M2bN1CpUiX3XAeBRxf2//znP645oTbqxRdf7E6SoVasWBE4++yzXZ8mCowUMB04cCAQa77//nt3YQ9/qBm01xz8oYcechdoNQHv0KGD6ysi1ObNm90FvXDhwq6JYO/evV1wEEp95LRt29Z9h9apgqZYXnZd8HRA60BWc8lq1aq5vjBCm0TG67JHWmY91AdMZu/jWseNGzd2x5IuGqG/EWvLvWrVKndRK1mypNtW6rNIJ+zQPk/icbnl2muvdfuw5kf7tI5jL7Dx6/ZOz7L7eZunJ7iJ9e2eQ//8+/wPAABAbKDODQAA8BWCGwAA4CsENwAAwFcIbgAAgK8Q3AAAAF8huAEAAL5CcAMAAHyF4AZAuqxYscJy5MgR7GI+FixYsMBatWrlBvdr3LhxtGfHrrnmGrvooouCz08//XQ3gjiA44vgBogTunAquBgyZEiK1z/99FP3enY0cOBAK1SokBvXJnyMmvD1poc3COIjjzziBirNamPHjrVHH300y4InAJER3ABxRBmKJ5980rZu3Wp+odGFj5UGpW3btq1Vq1bNSpUqlep0Z511lq1bt84WL15s//3vf+3hhx+2p59+OtPnJ1zJkiWtSJEimfZ9ANKH4AaIIx07dnSjrQ8ePDjVaXThDi+iGTp0qFWvXv2IDMATTzxh5cqVs+LFiwezGffcc4+7KFeuXNlGjBgRsSioTZs2LtA66aST7Icffkjx/h9//GFnn322FS5c2H331VdfbZs2bUpRVKMRtlVcU7p0aevSpUuqoy9rnjQf+fLlc8s0fvz44PvKxGhUdU2jv7XcqdHntd4UBN18881uPX7++ecp1sXjjz9uFStWdKOuS0JCgl1++eVu3Wh9XHjhha5ozpOcnGx9+/Z17yuwuvfee93o4aHCi6U0mvR9991nVapUcfOkLNKbb74Z/L7rrrvOjTheoEABNx8vvPBCiu36zjvv2GeffRbMRE2ePDld86rpWrRo4bJcmubUU0+1lStXprq+gHhHcAPEkVy5crmA5MUXX7TVq1f/q++aNGmSrV271n788Ud77rnnXBHPeeedZyVKlLCff/7ZbrrpJrvxxhuP+B0FP8p+zJkzx1q3bm3nn3++bd682b23bds2O/PMM61Jkyb266+/umBkw4YN7sIbShdpFRFNmzbNhg8fHnH+dGF/9tln7ZlnnrHffvvNBUEXXHCBy76IMjENGjRw86K/77777nQvu4KH0AyNirRUtDVhwgT78ssv7cCBA+73lHWZMmWKm08Fa8oAeZ/TvL399tv21ltv2dSpU23Lli32ySefpPm7PXv2tA8++MD+97//2fz58+3VV1913+sFcwrkPvzwQ/vrr79swIABdv/999uYMWPc+1o+rUcvC6WHgsyjzasCVgVv7du3d+txxowZdsMNN2TbokxkE5ky/CaALKcRxy+88EL3d6tWrdyIxfLJJ5+4Eao9AwcODDRq1CjFZ59//nk3unHod+l5cnJy8LW6desG2rVrF3x+8ODBQKFChQIffPCBe758+XL3O6Ejk2uE38qVKweefPJJ9/zRRx91I6KHSkhIcJ/zRoTX6MJNmjQ56vJWrFgx8Pjjj6d47ZRTTnEjEXu0nFre9K43jVQ/YcIEN4rz3XffHXxfI9cnJSUFP/Pee++59aHpPXpfoxt/88037nmFChUCTz311BHrwvut8JGUtfxaD/r99LrlllsCl1xyScRlSe+8avR5/e7kyZPT/btAvMsd7eAKQMap3o0yJBnJVoRT1iNnzn+StypCUjFTaJZIxS2JiYkpPqdsjSd37tzWvHlzl4WQefPm2ffffx/MRoTXj6lTp477u1mzZmnO244dO1xWScUnofRcv5FRysZonpTlUIbkyiuvTFGM1bBhQ5dJ8ug3lixZckR9mX379rnl2L59u8uctGzZ8oh1EV405VErM61TZVBSM2zYMJcJWrVqle3du9dlXo7WCuxo89q5c2dX9KbsTqdOnVyRnDJAFSpUSPN7gXhGcAPEodNOO81drPr37+8uXKEUsIRfYHVRD5cnT54Uz1VMEek1BQPptWvXLldMpeArXOjFVHU/jqczzjjDXnnlFRfAqF6NApFQ4fOj5VAA9v777x/xXWXKlDmmeVBRWFpGjRrlglUVdymAVLCiSs8qIkxLeuZVdaduv/12V0w4evRoe/DBB10RnJrRA35EcAPEKTUJ1129VwE29IK2fv16F+B49Soys2+an376yQVXovocqtSrCsLStGlT+/jjj13l5fAAIiOKFi3qghDVHwnNdOi5KsZmlIIXVd5NLy2HgoCyZcu6eYlEwZoCj/B1oc9GouyQAkVVwFb2JJyWTXVo/vOf/wRfU+YllIIzVTzO6LyK6kHpoYBYwdPIkSMJbuBbVCgG4pQulj169HCVU8Nb6GzcuNGeeuopd3FUUcfXX3+dab+r71PFWbWauuWWW1yz9Guvvda9p+eqWNu9e3f75Zdf3O9/88031rt37yMuykejisvKAOnCrcq+/fr1c0HaHXfcYVlN61UtudTqSJV0ly9f7locKfvhVbDWfCjAVD9DWhcKSlShOjUK+Hr16uXWlT7jfadXYbh27dquErbW16JFi+yhhx5y6zD8O1QpWOtDLdCUkTvavOq5AhpVJFYLqW+//dZVyj7xxBOzeC0C0UNwA8QxNYMOLzbSRevll192QUijRo1s5syZ/6puTjhd0PXQd6uVkJpU6+IqXrZFgYzqeigAU1NoNT8Ord+THro4q6m1WkPpe1Skot9SEJDVChYs6FqRVa1a1bp27erWqZppqx6Llx3RfKmZuwIWrxjp4osvTvN7VTR26aWXukCoXr161qdPH9u9e7d7Ty3T9FvdunVzdXnUAi00iyOaXpk61e1Rhk7r+mjzqvcVfF1yySWuzpNaSikI1e8BfpVDtYqjPRMAAACZhcwNAADwFYIbAADgKwQ3AADAVwhuAACArxDcAAAAXyG4AQAAvkJwAwAAfIXgBgAA+ArBDQAA8BWCGwAA4CsENwAAwFcIbgAAgPnJ/wMKEaJroxZd1AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_predicates = np.arange(1, 11) * len(population) * 4\n",
    "fp_results, tp_results = simulate_attacks(\n",
    "    execute_dp_query,\n",
    "    membership_attack_fig_1, \n",
    "    sample_size=5,\n",
    "    reference_size=50, \n",
    "    num_predicates=num_predicates, \n",
    "    num_trials=100,\n",
    "    delta=0.05)\n",
    "print(\"FPR\", fp_results)\n",
    "print(\"TPR\", tp_results)\n",
    "\n",
    "plot_fpr_tpr(num_predicates, fp_results, tp_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a31b93c",
   "metadata": {
    "cell_id": "eb5f3204-a607-4094-975b-87d7319d874f",
    "tags": []
   },
   "source": [
    "The resulting graphs show that the membership attack is thwarted. The response from the test is practically a coin-flip; it fails to identify Alice as a member of the sequestered dataset about half the time, and falsely identifies Anita as a member of the dataset about half the time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}