31:T6b6,md` # The Biggest Data Breaches of 2017 Commercial firms often hit the headlines for security breaches, but governments lose data too. To try and figure out whether the two are comparable in magnitude, I obtained a dataset of the biggest data breaches of 2017 from [Breach Level Index](http://breachlevelindex.com/data-breach-database). Looking at the data qualitatively, the largest breach of 2017 was from a commercial firm: in March, a faulty backup at spam giant River City Media [exposed the email addresses of an astonishing 1.4 billion people](https://mackeeper.com/blog/post/339-spammergate-the-fall-of-an-empire). But the second and third largest breaches were both from governments. In May, the Motor Vehicles Department in Kerala, India, [leaked the registration details of 20 million vehicle owners](http://english.mathrubhumi.com/news/kerala/mvd-database-compromised-vehicle-regn-details-leaked-1.1908611), and in June, a data analytics firm that helped Donald Trump's 2016 election campaign [exposed personal information on 198 million Americans](http://www.dw.com/en/deep-root-analytics-behind-data-breach-on-198-million-us-voters-security-firm/a-39318788). But in the notebook below, I've dived deeper into some more mathematical and visual analysis of how 2017 panned out in the world of data breaches. To read about digital data breaches and the individuals who fall through the cracks of identity systems, be sure to check out [the article that accompanies this notebook](https://howwegettonext.com/the-id-question-6fb3b56052b5). Plus, if you want to talk about digital identity in more detail, be sure to join our [ID Question Facebook group](https://www.facebook.com/groups/idquestion/). `32:T89a3,LICENSE = "GPL 3.0" // Applies to all the code and analysis except the Breach Level Index data itself /* GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. 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The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . */33:[] 2f:{"title":"The Biggest Data Breaches of 2017","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"md`\n## Grabbing the Data\nThe first thing to do is pull the data down from where it's stored, [on Github](https://raw.githubusercontent.com/Storythings/2017databreaches/master/breach_level_index.csv).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"data = d3.csv(\"https://raw.githubusercontent.com/howwegettonext/2017databreaches/master/breach_level_index.csv\")\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":51,"value":"md`## Analysing the Data\nFrom this, we can find all kinds of interesting things. Like the biggest breaches in the UK:\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"data.filter(breach => breach.location == \"United Kingdom\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":69,"value":"md`Or an array of just the organisation names from the biggest breaches in the UK.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":73,"value":"data.filter(breach => breach.location == \"United Kingdom\")\n .map(breach => breach.organisation)","pinned":true,"hidden":false,"mode":"ojs"},{"id":66,"value":"md`Or the total number of records leaked across all companies.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"d3.format(\",\") // Format with thousands\n (data.map(breach => breach.records = +breach.records) // Get an array of the records\n .reduce((total, records) => total + records)) // Reduce them to a total","pinned":true,"hidden":false,"mode":"ojs"},{"id":121,"value":"md`Or the average number of records leaked in each industry, for which the code is a little more complex.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":211,"value":"avgLeaksPerInd = {\n let output = []; // Create an empty array\n for (let industry of [...new Set(data.map(breach => breach.industry))]) { // For each unique industry\n let toPush = {};\n toPush[\"Industry\"] = industry;\n let leaks = data.filter(breach => breach.industry == industry) // Filter for that industry\n \t\t\t\t.map(breach => breach.records = +breach.records) // Convert array of strings to integers\n // Average the array\n toPush[\"Average Leaks\"] = leaks.reduce((total, records) => (total + records))/leaks.length;\n toPush[\"n\"] = leaks.length; // How many leaks in each category\n output.push(toPush);\n }\n // Sort the output from highest leaks to lowest\n output.sort((a, b) => b[\"Average Leaks\"] - a[\"Average Leaks\"]);\n return output;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":352,"value":"md`We can render that array in a nice table with a bit of Javascript:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":574,"value":"table_keys = Object.keys(avgLeaksPerInd[0])","pinned":true,"hidden":false,"mode":"ojs"},{"id":357,"value":"table = {\n // Set up the header, using the keys from above\n let header = table_keys[0] + \"|\" + table_keys[1] + \"|\" + table_keys[2] + \"\\n-|-|-\\n\" \n // Create an empty body, ready to be filled by the loop\n let body = \"\"\n // Loop over all the things in avgLeaksPerInd\n for (let item of avgLeaksPerInd) { \n let line = \"\"; // empty line, ready to be added to\n line += item[table_keys[0]]; // Add the industry\n line += \"|\"; // Add a pipe spacer\n line += d3.format(\",.0f\")(item[table_keys[1]]); // Add the average leaks\n line += \"|\"; // Add a pipe spacer\n line += d3.format(\",.0f\")(item[table_keys[2]]); // Add the number of leaks\n line += \"\\n\"; // Add newline character\n body += line; // Add the line we just created to the body\n }\n // Then glue the header to the body and return it in markdown format.\n return md`${header + body}`\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":491,"value":"md`## Removing the Outlier\nOne downside of taking averages is that get skewed by single outlier values. In this case, the enormous River City Media data breach is making it hard to compare across categories, so let's take it out and reanalyse the data with the same code as before.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":494,"value":"cleaned = data.filter(breach => breach.organisation != \"River City Media\") // Exclude River City Media","pinned":true,"hidden":false,"mode":"ojs"},{"id":506,"value":"clean_avgLeaksPerInd = {\n let output = []; // Create an empty object\n for (let industry of [...new Set(cleaned.map(breach => breach.industry))]) { // For each unique industry\n let toPush = {};\n toPush[\"Industry\"] = industry;\n let leaks = cleaned.filter(breach => breach.industry == industry) // Filter for that industry\n \t\t\t\t.map(breach => breach.records = +breach.records) // Convert array of strings to integers\n // Average the array\n toPush[\"Average Leaks\"] = leaks.reduce((total, records) => (total + records))/leaks.length;\n toPush[\"n\"] = leaks.length; // How many leaks in each cat\n output.push(toPush);\n }\n output.sort((a, b) => b[\"Average Leaks\"] - a[\"Average Leaks\"]);\n return output;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":509,"value":"clean_table = {\n // Set up the header, using the keys from above\n let header = table_keys[0] + \"|\" + table_keys[1] + \"|\" + table_keys[2] + \"\\n-|-|-\\n\" \n // Create an empty body, ready to be filled by the loop\n let body = \"\"\n // Loop over all the things in avgLeaksPerInd\n for (let item of clean_avgLeaksPerInd) { \n let line = \"\"; // empty line, ready to be added to\n line += item[table_keys[0]]; // Add the industry\n line += \"|\"; // Add a pipe spacer\n line += d3.format(\",.0f\")(item[table_keys[1]]); // Add the average leaks\n line += \"|\"; // Add a pipe spacer\n line += d3.format(\",.0f\")(item[table_keys[2]]); // Add the number of leaks\n line += \"\\n\"; // Add newline character\n body += line; // Add the line we just created to the body\n }\n // Then glue the header to the body and return it in markdown format.\n return md`${header + body}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":516,"value":"md`That's better. Now, we can see that when you remove the River City Media outlier, governments leaked more data than any other sector in 2017.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":321,"value":"md`## Charting\nNow let's chart those! First, we define the x and y scale functions.` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":323,"value":"x = d3.scaleBand() // Band scales are used for categorical data\n .domain(clean_avgLeaksPerInd.map(d => d[\"Industry\"])) // Domain is industry list\n .range([margin.left, width - margin.right]) // Range is horizontal chart space\n .padding(0.1) // A little padding - 10% of a bar width","pinned":true,"hidden":false,"mode":"ojs"},{"id":327,"value":"y = d3.scaleLinear() // A linear scale for the y-values\n .domain([0, d3.max(clean_avgLeaksPerInd, d => d[\"Average Leaks\"])]).nice() // Domain from 0 to max data\n .range([height - margin.bottom, margin.top]) // Range from bottom to top of chart","pinned":true,"hidden":false,"mode":"ojs"},{"id":329,"value":"md`Then we define the x-axis and y-axis functions.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":332,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`) // Move it to the bottom\n .call(d3.axisBottom(x) // Draw it...\n .tickSize(0).tickPadding(8)) // ...without the individual tick marks","pinned":true,"hidden":false,"mode":"ojs"},{"id":334,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`) // Move it to the left\n .call(d3.axisLeft(y)) // Draw it...\n .call(g => g.select(\".domain\").remove()) // ...without the axis line","pinned":true,"hidden":false,"mode":"ojs"},{"id":336,"value":"md`Finally, we just need to set the height of the chart and the margins, which you might notice that we've already referenced in the code above:`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":338,"value":"height = 500","pinned":true,"hidden":false,"mode":"ojs"},{"id":340,"value":"margin = ({top: 20, right: 0, bottom: 30, left: 70})","pinned":true,"hidden":false,"mode":"ojs"},{"id":344,"value":"md`Then we can finally plot the chart!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"chart = {\n const svg = d3.select(DOM.svg(width, height)); // Create an svg\n \n // The next bit is a normal D3 data join\n // See https://bost.ocks.org/mike/join/ for details\n svg.append(\"g\")\n .selectAll(\"rect\")\n .data(clean_avgLeaksPerInd)\n .enter()\n .append(\"rect\")\n .attr(\"x\", d => x(d[\"Industry\"]))\n .attr(\"y\", d => y(d[\"Average Leaks\"]))\n .attr(\"height\", d => y(0) - y(d[\"Average Leaks\"]))\n .attr(\"width\", x.bandwidth())\n .attr(\"fill\", // Double encoding the records with height and colour\n d => d3.scaleSequential(d3.interpolateBlues).domain([-5000000, 26000000])\n (d[\"Average Leaks\"]));\n \n svg.append(\"g\")\n .call(xAxis); // Add the x axis\n \n svg.append(\"g\")\n .call(yAxis); // Add the y axis\n \n return svg.node();\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":523,"value":"md`The \"other\" category still scores pretty highly, but the chart clearly shows that government records were almost twice as likely to be leaked as any other sector in 2017.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":525,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"md`This Observable notebook was created by [Duncan Geere](http://duncangeere.com/) for [the ID Question](https://youtube.com/watch?v=L_jWHffIx5E) and [How We Get To Next](http://www.howwegettonext.com). \n\nData came from [Breach Level Index](http://breachlevelindex.com/data-breach-database).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":315,"value":"d3 = require(\"https://d3js.org/d3.v5.js\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":669,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":688,"value":"md`Little hack to make sure tables work on mobile screens.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":679,"value":"html``","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}