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This repository hosts a persona based privacy threat modeling solution called Models of Applied Privacy or MAP.

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Comcast/xCOMPASS

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xCOMPASS (COMcast Privacy ASSistant)

xCOMPASS is a questionnaire developed from Models of Applied Privacy (MAP) personas. It consists of a set of questions, each requiring a "Yes" or "No" answer. Depending on the answers to the questions (based on the specifications of an application), the questionnaire is meant to help developers identify privacy engineering requirements specific to the application.

Quickstart

To learn more, please follow these links:

In this repository, you can also find an Excel spreadsheet (xCOMPASS Spreadsheet v1.0.xlsx) containing xCOMPASS questionnaire that you can use to identify privacy engineering requirements for your application. It uses simple Excel spreadsheet formulas to automatically evaluate your answers. Please watch the following 42-second video for a demo of the spreadsheet.

xCOMPASS.Quickstart.mp4

Why Do We Need It

It is key to identify privacy engineering requirements as early as possible in the software development lifecycle (SDL) of an application, preferrably when the application is being designed to incorporate privacy into its designed, namely privacy-by-design strategy. Unfortunately, identifying such requirements is challenging, mostly due to the following factors:

  1. It mostly involves human experts (i.e., threat modelers) with much manual effort.
  2. It is usually performed later in the SDL process, during which much development work has been finished.
  3. App developers are usually not familiar with privacy principles (e.g., privacy laws) that can guide the development process.

To address these limitations, we created xCOMPASS, an open-sourced framework that presents a solution that does not require much expertise/training in privacy domain to identify privacy engineering requirements during PTM.

  1. xCOMPASS presents a lightweight questionnaire (i.e., yes-no questions).
  2. It identifies privacy requirements based on the answers.
  3. It maps the requirements to privacy principles (e.g., privacy laws) and mitigation strategies (e.g., de-identification).

Who Can Benefit

We designed xCOMPASS for people who are not privacy experts. It can benefit people in the following roles:

  • Application developers
  • Product designers
  • Managers and organization leaders

Meanwhile, it certainly can also benefit privacy experts, such as:

  • Security and privacy engineers
  • Data protection engineers
  • Data governance engineers

and others that work with an application and would like to identify privacy engineering requirements for the application.

What I Can Do with It

xCOMPASS can be used in (but not limited to) the following use cases:

  • A developer or a team of developers that create a new application that collects personal information.
  • A developer or a team of developers that maintains applications that collect and store personal information.
  • A privacy engineer that analyzes and maintains privacy engineering requirements for systems and applications.
  • A data protection/governance engineer that works on protocols for data collection and usage in an organization.

Contribution

We welcome all kinds of contributions to this repository! Please have a look at CONTRIBUTING.md for further information and guidelines.

Maintainers

The list of maintainers of this GitHub repository is available in MAINTAINERS.md. Please consider becoming a maintainer! 😃

Roadmap

Roadmap information is available in ROADMAP.md.

List of Publications

Jayati Dev, Bahman Rashidi, Vaibhav Garg. Models of Applied Privacy (MAP): A Persona Based Approach to Threat Modeling. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23).

List of Talks

Visibility on Other Websites

xCOMPASS has been proudly listed as an open-sourced privacy engineering requirements identification tool on various websites, including:

License

Licensed under Apache 2.0.

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This repository hosts a persona based privacy threat modeling solution called Models of Applied Privacy or MAP.

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