2b:null 2c:["$","title",null,{"children":"Data Breaches according to their data sensitivity (A visualization critique) | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:T54c,## Data Abstraction: This dataset contains all of the biggest data breaches in the world (>30,000 records) from the year 2004 to 2024. - Dataset Type: It is a Table. It has 489 rows and 16 columns(attributes). Click [here](https://geni.us/databreaches) to view the data. - Attributes:
1) organization: Name of the organization that was breached. (Categorical) 2) alternative name: Another name for an organization. (Categorical) 3) records lost: The actual number of records that were lost in the data breach. (Quantitative, Sequential) 4) year: The year in which the data breach occurred. (Ordinal) 5) story: A description of the data breach. (Categorical) 6) sector: The sector in which the breach occurred. (Categorical) 7) data sensitivity: A scale from 1-5, with 1 being the least sensitive data and 5 being the most. (Ordinal) 8) displayed records: A derived quantitative attribute which rounds up the actual number of records lost, to display and easy to read number. (Derived, Quantitative, Sequential) 9) data sensitivity level: A derived categorical attribute which gives a label for each level of data sensitivity. (Derived, Ordinal) - Data Availability: Static Data32:T469,## How Abstraction: Marks:
1. Point (with filled area): Represents each data breach. 2. Text: The text on each point represents "organization" (Categorical) and "year" (Ordinal).
Channels:
1. Spatial Region (x-axis): The x-axis represents the "data sensitivity level" attribute (Derived, Ordinal). Starting from the least sensitive (EMAIL ADDRESSES, ETC) to the most sensitive data (FULL /SENSITIVE DETAILS) 2. Color hue: The color hue represents the "data sensitivity level" attribute (Derived, Ordinal). Each color indicating a different level of sensitivity. 3. Area(size): The area/size of each point represents the number of "records lost" (Quantitative, Sequential) in the data breach. 4. Color (Opacity): The fully opaque points represent the data for the filters (Sector - Categorical) & (Year - Ordinal) that we select (eg: 2004-2014), the half opacity points are the remaining data breaches that are not under the filter chosen. 5. On hover: Upon hovering on a point, the "story" (Categorical) attribute is also represented as text.33:T530,## Critique: 1. Positive: - The color hue for each level of sensitivity is an appropriate choice and correctly expresses the attribute. The use of red for the most sensitive data level is also good. - The area(size) of each point is a good way to represent the scale of breach and makes it easy to compare the larger and smaller breaches. - I think the controls given to the user are just the right amount, not too less and not too much, filters are easy to use and fairly straightforward. 2. Negative: - The smaller points are impossible to read, and often sectors which have breaches that are only very small can go unnoticed easily. - The legend is not clearly defined, the color scale for data sensitivity level is not mentioned and the scale on the x-axis (least sensitive to most) is not mentioned either. - When we hover over a point the description pops up but we lose the year in which it happened, this makes it impossible to identify which year the smaller breaches had happened in. - The y-axis could have been used to arrange the breaches in the order of "records lost", making it easier to separate larger and smaller breaches or there could have been an option to "sort by". - The number of breaches could have been mentioned on the visualization for each selected filter using the count function.34:[{"id":"f144774fbdb383d41a1ca9b001e42b5b28164cac19d333c6b5a625501beebc96b80488b9f0f5580f67b4259dc5894818c1f50466a9f8e5d6422079e8d5dbb4fd","href":"https://static.observableusercontent.com/files/f144774fbdb383d41a1ca9b001e42b5b28164cac19d333c6b5a625501beebc96b80488b9f0f5580f67b4259dc5894818c1f50466a9f8e5d6422079e8d5dbb4fd","name":"image.png","type":"image/png","lastModified":1727978537229.954,"size":663722},{"id":"a6e414409e5bdab6d72cf34a52327edf195da85aa77b03f0f750512d5ab3dddef957b255f1e1c8c88dd661080acd5b19c6e94b841cff5095d2a4f71892f25c19","href":"https://static.observableusercontent.com/files/a6e414409e5bdab6d72cf34a52327edf195da85aa77b03f0f750512d5ab3dddef957b255f1e1c8c88dd661080acd5b19c6e94b841cff5095d2a4f71892f25c19","name":"image@2.png","type":"image/png","lastModified":1727981974867.498,"size":257783},{"id":"7bf4e8264ea75ad60d48a84363888a2f63a1c21890e645ee079b02cfe380853118185c739f53bf0a631024146f8406169b0a1c464b4a518a8c3389126fdb461d","href":"https://static.observableusercontent.com/files/7bf4e8264ea75ad60d48a84363888a2f63a1c21890e645ee079b02cfe380853118185c739f53bf0a631024146f8406169b0a1c464b4a518a8c3389126fdb461d","name":"image@3.png","type":"image/png","lastModified":1727982040868.733,"size":576006},{"id":"d6568289f6e3939874b634862d91734c87dd2bfb30b01067ccee6fbe0c17a234d85a6dbc84e38a2a84103010236d2538f857d62fa0ad0ab52f55a3360573ee40","href":"https://static.observableusercontent.com/files/d6568289f6e3939874b634862d91734c87dd2bfb30b01067ccee6fbe0c17a234d85a6dbc84e38a2a84103010236d2538f857d62fa0ad0ab52f55a3360573ee40","name":"image@4.png","type":"image/png","lastModified":1727984001920.68,"size":164993},{"id":"59f124a7eff7fc4b730e8134259341105025c3cfeb57b57b93c15cbdb92aa263724577e8dc9ba92622c2a3df59211efead667f5da9e3dca2c075b6345e9d322b","href":"https://static.observableusercontent.com/files/59f124a7eff7fc4b730e8134259341105025c3cfeb57b57b93c15cbdb92aa263724577e8dc9ba92622c2a3df59211efead667f5da9e3dca2c075b6345e9d322b","name":"image@5.png","type":"image/png","lastModified":1727984068861.456,"size":168067}] 2f:{"id":"61e4632aa2b8fc87","slug":null,"title":"Data Breaches according to their data sensitivity (A visualization critique)","thumbnail":"64e5ab8aef11ccda7a264c358c6ad24f439fef6295b44d2bb8968e2666b4056e","access_level":"public","create_time":"$D2024-10-03T17:58:07.519Z","update_time":"$D2024-10-05T01:34:39.117Z","view_time":null,"first_public_time":"$D2024-10-05T01:30:36.213Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. 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Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Data Breaches according to their data sensitivity (A visualization critique)","cells":[{"id":0,"value":"# Data Breaches according to their data sensitivity (A visualization critique)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"This is a data visuzalization critique done by [Jonathan Tarun Rajasekaran](https://observablehq.com/d/8586f881717414c5) from Northeastern University, Oakland for the course CS7295 - Special Topics in Data Visualization under Professor [John Alexis Guerra Gomez](https://johnguerra.co/).\n\nThe visualization is based on a dataset of all of the world's biggest data breaches (Data breaches with more than 30,000 records lost were chosen for this visualization).\n
\n\nThis visualization was made by David McCandless, Tom Evans, Miriam Quick, Ella Hollowood, Christian Miles, Dan Hampson and Duncan Geere.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"intro","database":"$undefined"},{"id":18,"value":"## This is the visualization I have chosen from the linked website:\n
\nClick [here](https://informationisbeautiful.net/visualizations/worlds-biggest-data-breaches-hacks/#bysensitivity) to check out the original visualization","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":14,"value":"![image.png](${await FileAttachment(\"image.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"viz","database":"$undefined"},{"id":131,"value":"## Table of Contents\n\n1. [Introduction](#intro)\n2. [Visualization](#viz)\n3. [Contents](#cont)\n4. [Insights](#ins)\n
\n 4.1 Three Main Insights\n
\n 4.2 [Figure 1](#fig1)\n
\n 4.3 [Figure 1](#fig2)\n
\n 4.4 [Figure 1](#fig3)\n
\n 4.5 [Figure 1](#fig4)\n
\n5. [Data Abstraction](#data)\n
\n 5.1 Dataset Type\n
\n 5.2 Attributes\n
\n 5.3 Data Availability\n6. [Task Abstraction](#task)\n
\n 6.1 Task 1\n
\n 6.1.1 Task 1.1 (sub-task)\n
\n 6.2 Task 2\n
\n 6.2.1 Task 2.1 (sub-task)\n
\n 6.3 Task 3\n7. [How Abstraction](#how)\n
\n 7.1 Marks\n
\n 7.2 Channels\n8. [Users](#user)\n9. [Critique](#crit)\n
\n 9.1 Positive\n
\n 9.2 Negative\n10. [YouTube Video](#yt)\n11. [Tweet](#x)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"cont","database":"$undefined"},{"id":34,"value":"## Insights\n\n1. Compared to the years [(2004 - 2014)](#fig1) , the number of data breaches and their scale have almost tripled in the last 10 years [(2015 - 2024)](#fig2). The biggest change being in the health and personal data sensitivity level (exponential increase in number of breaches and scale).\n\n2. The military sector have the one of the lowest breaches - only 5 over the last 20 years, however what is interesting is that all 5 breaches happened to an organization from the US. [(Figure 3)](#fig3)\n\n3. The Legal Sector seems to be one of the most secure sectors having only one minor(compared to the others on the list) breach across the last 20 years. [(Figure 4)](#fig4)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"ins","database":"$undefined"},{"id":41,"value":"## Figure 1 (2004-2014)\n\n![image@2.png](${await FileAttachment(\"image@2.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"fig1","database":"$undefined"},{"id":43,"value":"## Figure 2 (2015 - 2024)\n\n![image@3.png](${await FileAttachment(\"image@3.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"fig2","database":"$undefined"},{"id":37,"value":"## Figure 3 (Military sector)\n\n![image@5.png](${await FileAttachment(\"image@5.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"fig3","database":"$undefined"},{"id":51,"value":"## Figure 4 (Legal Sector)\n\n![image@4.png](${await FileAttachment(\"image@4.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"fig4","database":"$undefined"},{"id":58,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"data","database":"$undefined"},{"id":69,"value":"## Task Abstraction:\n\nT1: To Compare the number and scale of data breaches between 2004-2014 and 2015-2024. (Compare, Distribution)\n\nT1.1 (sub-task): To Identify which level of data sensitivity has had the biggest increase in data breaches (by number and scale). (Identify, Trend/Features)\n\nT2: To Identify the number of breaches in the Military sector. (Identify, Features)\n\nT2.1 (sub-task): Which country's organizations have had the most breaches in the military sector. (Identify, Outlier)\n\nT3: To Identify the most secure sector (least number of data breaches). (Identify, Outlier) ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"task","database":"$undefined"},{"id":85,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"how","database":"$undefined"},{"id":104,"value":"## Users (Target Audience):\n\n- Organizations: Any organization can check data breaches in their own sector in the last 20 years, and then go look them up to see why they happened and take preventive actions.\n- General Public: Anyone using products from any one of these organizations (Eg: Facebook, LinkedIn) need to be aware if their data has been breached before.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"user","database":"$undefined"},{"id":106,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"crit","database":"$undefined"},{"id":118,"value":"## YouTube Video\n\nCheck out my video explaining the insights and the critique [here](https://youtu.be/YIYaIYp6MQ8).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"yt","database":"$undefined"},{"id":121,"value":"## Tweet\n\nClick [here](https://x.com/JonathanTa84074/status/1842377585738785259) to check out my tweet.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"x","database":"$undefined"}],"files":"$W34","stdlib":"1"}} 30:"$2f:body"