{
 "cells": [
  {
   "attachments": {
    "PETS2019%20rump.001.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Truncated MAC address mobility measurement\n",
    "\n",
    "We take an idealised model of Euston station, inspired by a case study from the [Review of the TfL WiFi pilot](http://content.tfl.gov.uk/review-tfl-wifi-pilot.pdf). There are two locations (A and B), and some customers travel between them by the slow path (taking 3–5 minutes) and some by the fast path (taking 1–3 minutes). Some customers also arrive at one location and leave again at the same location. From measurements of MAC addresses at locations A and B, timestamped with second precision, we wish to infer what proportion of customers take each path.\n",
    "\n",
    "We consider two scenarios. Firstly, we assume that the full 48-bit MAC address is recorded, and therefore, there is a negligible probability of two customers having the same MAC address. Secondly, we assume that for privacy reasons the recorded MAC address is truncated to 16 bits and therefore many measurements of different customers will appear to have the same MAC address.\n",
    "\n",
    "The question we aim to address is that, in the privacy-preserving case, how much worse are the inferred conclusions? The simulation is quite basic, not very performant, and the inference algorithm is particularly simple-minded. This work is intended as a starting point for discussion rather than being useful in itself.\n",
    "\n",
    "![PETS2019%20rump.001.png](attachment:PETS2019%20rump.001.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Copyright 2019 Steven J. Murdoch\n",
    "### \n",
    "### Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "### you may not use this file except in compliance with the License.\n",
    "### You may obtain a copy of the License at\n",
    "### \n",
    "###   http://www.apache.org/licenses/LICENSE-2.0\n",
    "### \n",
    "### Unless required by applicable law or agreed to in writing, software\n",
    "### distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "### WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "### See the License for the specific language governing permissions and\n",
    "### limitations under the License.\n",
    "\n",
    "## Set up environment and parameters\n",
    "import random\n",
    "import math\n",
    "import secrets\n",
    "import collections\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## For each scenario, how many simulations do we run?\n",
    "NUMSIMS = 100\n",
    "## 1 customer per second is vaguly plausible for Euston's 42 million annual customers\n",
    "RATE = 1 # people per second\n",
    "## How long to run each simulation\n",
    "TIME = 18 * 60 * 60 # 18 hours, in seconds\n",
    "## A small number for dealing with floating point error\n",
    "EPSILON = 0.0000001\n",
    "\n",
    "## Average time for paths (from figures on p34 in http://content.tfl.gov.uk/review-tfl-wifi-pilot.pdf)\n",
    "TIME_FAST = 120 # fast path in seconds\n",
    "TIME_SLOW = 240 # slow path in seconds\n",
    "TIME_STDDEV = 30 # standard deviation for path (chosen arbitrarily)\n",
    "TIME_CUTOFF = 180 # over which duration to be assume a path was long\n",
    "\n",
    "## Probabilities for each speed (from figures on p34 in http://content.tfl.gov.uk/review-tfl-wifi-pilot.pdf)\n",
    "PF=0.68 ## That a random customer will take the fast path\n",
    "PS=1-PF ## That a random customer will take the slow path\n",
    "assert(abs(1-(PF+PS)) < EPSILON)\n",
    "\n",
    "## Probabilities for each path (arbitrarily selected)\n",
    "PAB=0.2 ## From A to B\n",
    "PBA=0.4 ## From B to A\n",
    "PAA=0.1 ## Enter and exit at A\n",
    "PBB=0.3 ## Enter and exit at B\n",
    "assert(abs(1-(PAB+PBA+PAA+PBB)) < EPSILON)\n",
    "\n",
    "## Probabilities for each case (assumes independence)\n",
    "PAB_F=PAB*PF ## A to B (fast)\n",
    "PAB_S=PAB*PS ## A to B (slow)\n",
    "PBA_F=PBA*PF ## B to A (fast)\n",
    "PBA_S=PBA*PS ## B to A (slow)\n",
    "assert(abs(1-(PAB_F+PAB_S+PBA_F+PBA_S+PAA+PBB)) < 0.0001)\n",
    "\n",
    "## Calculate cumulative probabilities for each case to help select biased random values\n",
    "C1 = PAB_F      # A->B (fast)\n",
    "C2 = PAB_S + C1 # A->B (slow)\n",
    "C3 = PBA_F + C2 # B->A (fast)\n",
    "C4 = PBA_S + C3 # B->A (slow)\n",
    "C5 = PAA   + C4 # A->A\n",
    "C6 = PBB   + C5 # B->B\n",
    "assert (abs(1 - C6) < EPSILON)\n",
    "\n",
    "## Enumeration for the cases\n",
    "CASE_ABF = 1\n",
    "CASE_ABS = 2\n",
    "CASE_BAF = 3\n",
    "CASE_BAS = 4\n",
    "CASE_AA  = 5\n",
    "CASE_BB  = 6\n",
    "MAX_CASES = CASE_BB\n",
    "\n",
    "## Enumeration for the locations\n",
    "A = 0\n",
    "B = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def whichcase():\n",
    "    '''Return a case such that each case is selected with the probabilities\n",
    "    specified by C1 .. C6'''\n",
    "    n = random.random()\n",
    "    if n < C1:\n",
    "        return 1\n",
    "    elif n < C2:\n",
    "        return 2\n",
    "    elif n < C3:\n",
    "        return 3\n",
    "    elif n < C4:\n",
    "        return 4\n",
    "    elif n < C5:\n",
    "        return 5\n",
    "    else:\n",
    "        return 6\n",
    "    \n",
    "def waitingtime(isfast):\n",
    "    '''Return the travel time, taking the fast route if isfast is True and slow otherwise'''\n",
    "    if isfast:\n",
    "        return round(np.random.normal(TIME_FAST, TIME_STDDEV))\n",
    "    else:\n",
    "        return round(np.random.normal(TIME_SLOW, TIME_STDDEV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runsim(bits):\n",
    "    '''Run a single simulation of MAC address measurement of length bits.\n",
    "    Returns (entry events, exit events, MAC addresses seen)'''\n",
    "    \n",
    "    ## For each case, how often has it occurred\n",
    "    cases = collections.defaultdict(int)\n",
    "    ## For each MAC, a list of (time, location) of entry events\n",
    "    evin = collections.defaultdict(list)\n",
    "    ## For each MAC, a list of (time, location) of exit events\n",
    "    evout = collections.defaultdict(list)\n",
    "    ## What MAC addresses have we seen\n",
    "    macs = set()\n",
    "    \n",
    "    ## Simulate each second\n",
    "    for tick in range(TIME):\n",
    "        ## How many people arrived this second?\n",
    "        npeople = np.random.poisson(RATE)\n",
    "        for i in range(npeople):\n",
    "            ## Assume random MAC addresses -- not true but we are well away from\n",
    "            ## the birthday paradox point for 48 bit MAC addresses\n",
    "            mac = secrets.randbits(bits)\n",
    "            macs.add(mac)\n",
    "            \n",
    "            ## Generate a random case\n",
    "            case = whichcase()\n",
    "            cases[case] += 1\n",
    "            \n",
    "            ## Log the entry event at this tick \n",
    "            if case == CASE_ABF or case == CASE_ABS or case == CASE_AA:\n",
    "                evin[mac].append((tick, A)) # from A\n",
    "            else:\n",
    "                evin[mac].append((tick, B)) # from B\n",
    "                \n",
    "            ## If there is an exit event, find the time and log it\n",
    "            if case == CASE_ABF:\n",
    "                waiting = waitingtime(True)\n",
    "                evout[mac].append((tick + waiting, B)) \n",
    "            elif case == CASE_ABS:\n",
    "                waiting = waitingtime(False)\n",
    "                evout[mac].append((tick + waiting, B)) \n",
    "            elif case == CASE_BAF:\n",
    "                waiting = waitingtime(True)\n",
    "                evout[mac].append((tick + waiting, A))\n",
    "            elif case == CASE_BAS:\n",
    "                waiting = waitingtime(False)\n",
    "                evout[mac].append((tick + waiting, A))\n",
    "                \n",
    "    return evin, evout, macs\n",
    "\n",
    "def runinfer(evin, evout, macs):\n",
    "    '''Given (entry events, exit events, MAC addresses seen), infer paths taken.\n",
    "    Returns a mapping beween case ID and number of occurrences'''\n",
    "    cases = collections.defaultdict(int)\n",
    "    \n",
    "    ## For each MAC address, which path was taken? For simplicity, we only look at\n",
    "    ## the first time this MAC address enters and the first time it leaves. When\n",
    "    ## MAC addresses are unique, this is fine but when there are collisions this will\n",
    "    ## mean that real events will be missed\n",
    "    for mac in macs:\n",
    "        ins = evin[mac] # Entry events for this MAC address\n",
    "        outs = evout[mac] # Exit events for this MAC address\n",
    "        \n",
    "        ## No exit, so assume AA or BB based on where MAC first seen\n",
    "        if len(outs) == 0: ## AA or BB\n",
    "            if ins[0][1] == A:\n",
    "                case = CASE_AA\n",
    "            else:\n",
    "                case = CASE_BB\n",
    "                \n",
    "        ## MAC seen entering A and exiting at B\n",
    "        elif ins[0][1] == A and outs[0][1] == B:\n",
    "            duration = outs[0][0] - ins[0][0]\n",
    "            if duration > 0 and duration <= TIME_CUTOFF:\n",
    "                case = CASE_ABF\n",
    "            else:\n",
    "                case = CASE_ABS\n",
    "                \n",
    "        ## MAC seen entering B and exiting at A\n",
    "        elif ins[0][1] == B and outs[0][1] == A:\n",
    "            duration = outs[0][0] - ins[0][0]\n",
    "            if duration > 0 and duration <= TIME_CUTOFF:\n",
    "                case = CASE_BAF\n",
    "            else:\n",
    "                case = CASE_BAS\n",
    "        \n",
    "        ## Inference has failed, e.g. where ins = [(11611, A), (60964, B)]\n",
    "        ## and out = [(61129, A)], there is a collision between A->A and A->B \n",
    "        ## but we only look at the first entry event and first out event\n",
    "        else:\n",
    "            case = 0\n",
    "            \n",
    "        ## Record the inferred case\n",
    "        cases[case] += 1\n",
    "        \n",
    "    return cases\n",
    "\n",
    "def runscenario(bits):\n",
    "    '''Run a set of simulations for a scenario of MACs of length bits'''\n",
    "    ## For each case, a list of results of each simulation showing \n",
    "    ## the proportion of the MAC addresses inferred to belong to this case\n",
    "    stats = collections.defaultdict(list)\n",
    "    for sim in range(NUMSIMS):\n",
    "        ## Run the simulation to get events and MAC addresses\n",
    "        evin, evout, macs = runsim(bits)\n",
    "        ## From the events and MAC addresses infer the occurrences of each case\n",
    "        cases = runinfer(evin, evout, macs)\n",
    "        ## Number of occurrence of all cases (excluding unknown)\n",
    "        total = sum([cases.get(i, 0) for i in range(1, MAX_CASES + 1)])\n",
    "        for i in range(0, MAX_CASES + 1):\n",
    "            ## Compute proportion for each case in this simulation\n",
    "            stats[i].append(cases.get(i, 0) / total * 100.0)\n",
    "    return stats\n",
    "\n",
    "def doplot(stats, axes):\n",
    "    '''Plot the results of a scenario, with stats being the output of runscenario and\n",
    "    axes is a matplotlib axes'''\n",
    "    ## The data from the simulations\n",
    "    data = [sorted(stats.get(i,[0])) for i in range(0, MAX_CASES + 1)]\n",
    "    axes.violinplot(data)\n",
    "    ## The true values\n",
    "    axes.scatter(range(1, MAX_CASES + 2), [100.0 * x for x in [0, PAB_F, PAB_S, PBA_F, PBA_S, PAA, PBB]],\n",
    "                 marker='o', color='red', s=30, zorder=3)\n",
    "    ## Label the figure\n",
    "    axes.set_xticks(range(1, MAX_CASES + 2))\n",
    "    axes.set_xticklabels(['unknown', 'A→B (fast)', 'A→B (slow)', 'B→A (fast)', 'B→A (slow)', 'A→A', 'B→B'])\n",
    "    axes.set_xlabel(\"Case\")\n",
    "    axes.set_ylabel(\"Occurrence (%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Run the two scenarios (this will take a while)\n",
    "stats48 = runscenario(48)\n",
    "stats16 = runscenario(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"802\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "## Plot the non-privacy-preserving case\n",
    "fig, axes = plt.subplots(2, 1, figsize=(9, 12))\n",
    "doplot(stats48, axes[0])\n",
    "axes[0].set_title(\"Tracking 48 bit MAC addresses\");\n",
    "\n",
    "## Plot the privacy-preserving case\n",
    "doplot(stats16, axes[1])\n",
    "axes[1].set_title(\"Tracking 16 bit MAC addresses\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tubemap",
   "language": "python",
   "name": "tubemap"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}