awesome-privacy-engineering

Awesome Privacy Engineering

• 10 Examples of Manipulative Consent RequestsDeceptive Design Patterns

  Blog post that illustrates ten examples of manipulative consent patterns in cookie banners.

• Adding a Tag-Based PII Policy in ClouderaTagging Personally Identifiable Information

  How to add a PII tag-based policy. In this example, the author creates a tag-based policy for objects tagged "PII" in Atlas.

• Adversarial Robustness Toolbox (ART)Machine Learning and Algorithmic Bias

  Python library from the Linux Foundation AI & Data Foundation (LF AI & Data) that enables developers and researchers to defend and evaluate machine learning models and applications against the adversarial threats of evasion, poisoning, extraction, and inference.

• AequitasMachine Learning and Algorithmic Bias

  An open source bias audit toolkit developed by the Center for Data Science and Public Policy at University of Chicago, can be used to audit the predictions of machine learning based risk assessment tools to understand different types of biases, and make informed decisions about developing and deploying such systems.

• A Friendly, Non-Technical Introduction to Differential PrivacyDifferential Privacy and Federated Learning

  Blog post that provides simple explanations for the core concepts behind differential privacy.

• Aggregating Over Anonymized DataPrivacy Tech Series by Lea Kissner