Awesome Privacy Engineering [](https://awesome.re)

Books

• The Privacy Engineer's Manifesto: Getting from Policy to Code to QA to Value (Michelle Dennedy, Jonathan Fox, Tom Finneran)
• Information Privacy Engineering and Privacy by Design: Understanding Privacy Threats, Technology, and Regulations Based on Standards and Best Practices (William Stallings)
• The Algorithmic Foundation of Differential Privacy (Cynthia Dwork, Aaron Roth)
• Building an Anonymization Pipeline: Creating Safe Data (Luk Arbuckle, Khaled El Emam)
• Strategic Privacy by Design (R. Jason Cronk)
• The Architecture of Privacy: On Engineering Technologies that Can Deliver Trustworthy Safeguards (Courtney Bowman, Ari Gesher, John K. Grant, Daniel Slate, Elissa Lerner)
• Data Privacy: A Runbook for Engineers (Nishant Bhajaria)
• Privacy Design Strategies (The Little Blue Book) (Jaap-Henk Hoepman)
• Privacy Is Hard and Seven Other Myths: Achieving Privacy through Careful Design (Jaap-Henk Hoepman)
• Privacy Engineering: A Dataflow and Ontological Approach (Ian Oliver)
• Practical Data Privacy (Katharine Jarmul) - data-privacy)
• Strategic Privacy by Design (R. Jason Cronk)
• Threat Modeling: A Practical Guide for Development Teams (Izar Tarandach, Matthew J. Coles)
• Privacy Is Hard and Seven Other Myths: Achieving Privacy through Careful Design (Jaap-Henk Hoepman)
• Threat Modeling: Designing for Security (Adam Shostack)

Career

• What Does a Privacy Engineer Do, Anyway?
• What to Expect in a Privacy Interview
• Ethyca's Privacy Engineering Job Board
• What Does a Privacy Engineer Do, Anyway?

Conferences

• PEPR 2023 Conference
• PEPR 2022 Conference - 5ETT)
• PEPR 2021 Conference
• PEPR 2020 Conference
• PEPR 2019 Conference
• USENIX Enigma Conference
• Symposium on Usable Privacy and Security (SOUPS)
• International Workshop on Privacy Engineering (IWPE)
• PEPR 2024 Conference
• PEPR 2025 Conference - 1Es6pKnOZ)
• USENIX Conference on Privacy Engineering Practice and Respect (PEPR)

Courses

• OpenMined Courses
• Data Privacy and Anonymization in R - Datacamp course that covers publicly releasing data sets with a differential privacy guarantee.
• Data Privacy and Anonymization in Python - Datacamp course on learning to process sensitive information with privacy-preserving techniques.
• Secure and Private AI (Udacity) - Udacity course that covers how to extend PyTorch with the tools necessary to train AI models that preserve user privacy.
• Privacy-Conscious Computer Systems - This class at Brown University (CSCI 2390) focuses on how to design computer systems that protect users' privacy.
• Privacy by Design: Data Classification - LinkedIn Learning course by Nishant Bhajaria.
• Privacy by Design: Data Sharing - LinkedIn Learning course by Nishant Bhajaria.
• Implementing a Privacy, Risk, and Assurance Program - LinkedIn Learning course by Nishant Bhajaria.
• Data Protocol - Courses to teach developers and technical professionals how to build products responsibly and partner with platforms effectively.
• Carnegie Mellon University - Privacy Engineering Certificate - Four-week certificate program that revolves around a combination of mini-tutorials, class discussions, and hands-on exercises designed to ensure that students develop practical knowledge of all key privacy engineering areas.
• Technical Privacy Masterclass - In four modules, this course from Privado is designed to deliver privacy leaders and their teams with an overview of the pillars of a proactive privacy program.
• Privacy Quest - A gamified approach to learning about privacy engineering, Privacy Quest uses challenges and competitions to build your privacy and security knowledge.
• Hitchhiker's Guide to Privacy Engineering - The goal of this creative privacy project is to offer a fun, engaging, and immersive privacy learning experience for privacy lawyers to improve their technical privacy skills.
• Privacy-Conscious Computer Systems - This class at Brown University (CSCI 2390) focuses on how to design computer systems that protect users' privacy.
• Data Protocol - Courses to teach developers and technical professionals how to build products responsibly and partner with platforms effectively.
• Privacy Quest - A gamified approach to learning about privacy engineering, Privacy Quest uses challenges and competitions to build your privacy and security knowledge.
• Compliance Detective - A gamified approach to learning about privacy engineering, Compliance Detective (formerly Privacy Quest) uses challenges and competitions to build your privacy and security knowledge.
• Implementing a Privacy, Risk, and Assurance Program - LinkedIn Learning course by Nishant Bhajaria.
• Practical Data Ethics - This class was originally taught in-person at the University of San Francisco Data Institute in January-February 2020.

Data Deletion, Data Mapping, and Data Subject Access Requests

• Deleting Data Distributed Throughout Your Microservices Architecture - Microservices architectures tend to distribute responsibility for data throughout an organization. This poses challenges to ensuring that data is deleted.
• Handling Data Erasure Requests in Your Data Lake with Amazon S3 Find and Forget - Amazon S3 Find and Forget enables you to find and delete records automatically in data lakes on Amazon S3.
• How to Delete User Data in an AWS Data Lake - This post walks through a framework that helps you purge individual user data within your organization’s AWS hosted data lake, and an analytics solution that uses different AWS storage layers, along with sample code targeting Amazon S3.
• Detecting PII Using Amazon Comprehend - To detect entities that contain personally identifiable information (PII) in a document, use the Amazon Comprehend DetectPiiEntities operation.
• Detecting PII Using Amazon Comprehend - Using Amazon Comprehend to detect entities that contain personally identifiable information (PII) in a text document.
• Deleting Data Distributed Throughout Your Microservices Architecture - Microservices architectures tend to distribute responsibility for data throughout an organization. This poses challenges to ensuring that data is deleted.
• Amazon S3 Find and Forget
• Data Purging AWS Data Lake
• OpenDSR - A common framework enabling companies to work together to protect consumers' privacy and data rights (formerly known as OpenGDPR.)
• PrivacyBot - PrivacyBot is a simple automated service to initiate CCPA deletion requests with data brokers. (deprecated)
• Cookie Consent - An opensource, lightweight JavaScript plugin for alerting users about the use of cookies on a website. It is designed to help quickly comply with the European Union Cookie Law, CCPA, GDPR and other privacy laws.
• Fides - An open-source tool that allows you to easily declare your systems' privacy characteristics, track privacy related changes to systems and data in version control, and enforce policies in both your source code and your runtime infrastructure.
• Fideslang - Open-source description language for privacy to declare data types and data behaviors in your tech stack in order to simplify data privacy globally. Supports GDPR, CCPA, LGPD and ISO 19944.
• Fidesops - DSAR Orchestration: Privacy Request automation to fulfill GDPR, CCPA, and LGPD data subject requests. (deprecated)
• Privado - Privado is an open source static code analysis tool to discover data flows in the code. It detects the personal data being processed, and further maps the journey of the data from the point of collection to going to interesting sinks such as third parties, databases, logs, and internal APIs.
• Octopii - Octopii is an open-source AI-powered PII scanner that can look for image assets such as Government IDs, passports, photos and signatures in a directory.
• Data Profiler - DataProfiler is a Python library created by Capital One to make data analysis, monitoring, and sensitive data detection easy.
• PII Catcher - Scan databases and data warehouses for PII data. Tag tables and columns in data catalogs like Amundsen and Datahub.
• Best Practices: GDPR and CCPA Compliance Using Delta Lake - Article that describes how to use Delta Lake on Databricks to manage General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) compliance for a data lake.
• Klaro! - Klaro is a simple consent management platform (CMP) and privacy tool that helps you to be transparent about the third-party applications on your website.

Deceptive Design Patterns

• Deceptive Design Patterns - Deceptive design patterns (also known as "dark patterns") are tricks used in websites and apps that make you do things that you didn't mean to, like buying or signing up for something.
• The Dark Side of UX Design - Practitioner-identified examples of stakeholder values superseding user values.
• Dark Patterns Tipline - Gallery of deceptive patterns identified and submitted by individuals.
• 10 Examples of Manipulative Consent Requests - Blog post that illustrates ten examples of manipulative consent patterns in cookie banners.

De-Identification and Anonymization

• A Visual Guide to Practical Data De-Identification (FPF Infographic)
• Redacting Sensitive Information with User-Defined Functions in Amazon Athena - Amazon Athena supports user-defined functions, a feature that enables you to write custom scalar functions and invoke them in SQL queries.
• Anonymize Your Data Using Amazon S3 Object Lambda - Leverage AWS S3 Object Lambdas in order to anonymize data.
• Static Data Masking for Azure SQL Database and SQL Server - Microsoft's Static Data Masking is a data protection feature that helps users sanitize sensitive data in a copy of their SQL databases. It is compatible with SQL Server (SQL Server 2012 and newer), Azure SQL Database (DTU and vCore-based hosting options, excluding Hyperscale), and SQL Server on Azure Virtual Machines.
• ARX Data Anonymization Tool - ARX is a comprehensive open source software for anonymizing sensitive personal data.
• UTD Anonymization ToolBox - UT Dallas Data Security and Privacy Lab compiled various anonymization methods into a toolbox for public use by researchers.
• Data Anonymizer Extension for PostgreSQL - A set of SQL functions that remove personally identifiable values from a PostgreSQL table and replace them with random-but-plausible values.
• Anonymizer MySQL - This simple tool will allow you to make anonymizerd clone of your database.
• Singapore Guide to Anonymization - The Singapore Personal Data Protection Commission (PDPC) has published the Guide on Basic Anonymization to provide more practical guidance for businesses on how to appropriately perform basic anonymization and de-identification of various datasets.
• Transforming Data in Google Cloud Platform - This reference covers the available de-identification techniques, or transformations, that can be applied in Google Cloud's Data Loss Prevention (i.e., redaction, replacement, masking, crypto-based tokenization, bucketing, date shifting, and time extraction).
• k-anonymity
• k-map
• l-diversity
• delta-presence
• Technical Privacy Metrics: a Systematic Survey - Paper by Isabel Wagner and David Eckhoff that discusses over 80 privacy metrics and introduces categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. They also present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario.
• Data Anonymization Tool - The Singapore PDPC has launched a free Data Anonymization tool to help organizations transform simple datasets by applying basic anonymization techniques.
• UTD Anonymization ToolBox - UT Dallas Data Security and Privacy Lab compiled various anonymization methods into a toolbox for public use by researchers.
• Presidio - Context aware, pluggable and customizable PII anonymization service for text and images, developed by Microsoft.
• AWS AI-Powered Health Data Masking - The AI-Powered Health Data Masking solution in the AWS Solutions Library helps healthcare organizations identify and mask health data in images or text. (deprecated)
• Kodex - An open-source toolkit for privacy and security engineering. It helps you to automate data security and data protection measures in your data engineering workflows.
• Anonimatron - Free, extendable, open source data anonymization tool.
• MySQL Data Anonymizer - MySQL Data Anonymizer is a PHP library that anonymizes your data in the database.
• Anonymizer - Anonymizer is a universal tool to create anonymized DBs for projects.
• anonymize-it - The Elastic Machine Learning Team's general purpose tool for suppression, masking, and generalization of fields to aid data pseudonymization.
• Masked AI - Python SDK and CLI wrappers that enable safer usage of public large language models (LLMs) like OpenAI/GPT4 by removing sensitive data from prompts and replacing it with fake data before submitting to the OpenAI API.
• t-closeness
• Data Anonymization Tool - The Singapore PDPC has launched a free Data Anonymization tool to help organizations transform simple datasets by applying basic anonymization techniques.
• NIST Privacy Engineering Program - De-Identification Tools
• k-anonymity
• k-map
• l-diversity
• delta-presence
• Transforming Data in Google Cloud Platform - This reference covers the available de-identification techniques, or transformations, that can be applied in Google Cloud's Data Loss Prevention (i.e., redaction, replacement, masking, crypto-based tokenization, bucketing, date shifting, and time extraction).
• Singapore Guide to Anonymization - The Singapore Personal Data Protection Commission (PDPC) has published the Guide on Basic Anonymization to provide more practical guidance for businesses on how to appropriately perform basic anonymization and de-identification of various datasets.
• Singapore Guide to Anonymization - The Singapore Personal Data Protection Commission (PDPC) has published the Guide on Basic Anonymization to provide more practical guidance for businesses on how to appropriately perform basic anonymization and de-identification of various datasets.
• myanon - A streaming anonymizer for MySQL dump files. Reads mysqldump from stdin and writes an anonymized version to stdout. Supports deterministic hashing, fixed values, JSON field anonymization, and Python extensions.

Designing for Trust with Users

• Data Permissions Catalogue - Catalogue created by the data consultancy IF to help teams make decisions about how, when, and why to collect and use data about people.
• Privacy Patterns - UC Berkeley collection of design patterns attempting to standardize language for privacy-preserving technologies, document common solutions to privacy problems, and help designers identify and address privacy concerns.
• How to Protect Your Users with the Privacy by Design Framework - Developers can help to defend their users’ personal privacy by adopting the Privacy by Design (PbD) framework.
• The UX Guide to Getting Consent - Short guide by the International Association of Privacy Professionals (IAPP) about obtaining consent under the EU's GDPR.
• Creepiness-Convenience Tradeoff - As people consider whether to use the new "creepy" technologies, they do a type of cost-benefit analysis weighing the loss of privacy against the benefits they will receive in return.
• Building a Privacy Policy Users Actually Want to Read - Creation of a user-friendly privacy notice through privacy journeying and using a layered notice approach.
• Part 1: Common Concerns and Privacy in Web Forms
• Part 2: Better Cookie Consent Experiences
• Part 3: Better Notifications UX and Permissions Requests
• Part 4: Privacy-Aware Design Framework
• Lean Privacy Review - Carnegie Mellon University researchers developed a fast, easy method to catch privacy issues early in a system’s development process by gathering feedback from users.
• Contract Design Pattern Library - Library of guidelines, explanations, and examples to inspire and support you in exploring user-friendly approaches to contract simplification and visualization.
• Lean Privacy Review - Carnegie Mellon University researchers developed a fast, easy method to catch privacy issues early in a system’s development process by gathering feedback from users.

Differential Privacy and Federated Learning

• A Friendly, Non-Technical Introduction to Differential Privacy - Blog post that provides simple explanations for the core concepts behind differential privacy.
• A List of Real-World Uses of Differential Privacy - Blog post that compiles a list of real-world deployments of differential privacy, with their privacy parameters.
• Differential Privacy at the U.S. Census Bureau - Video on how differential privacy is being implemented in the U.S. Census.
• Privacy-Preserving AI - Video on Privacy Preserving AI (Andrew Trask) | MIT Deep Learning Series
• Microsoft's SmartNoise - This toolkit uses state-of-the-art differential privacy techniques to inject noise into data, to prevent disclosure of sensitive information and manage exposure risk.
• diffpriv: Easy Differential Privacy - R package that is an implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006).
• sdcMicro: Statistical Disclosure Control Methods for Anonymization of Microdata and Risk Estimation - R package that can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files.
• PPRL: Privacy Preserving Record Linkage - R package that is a toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques.
• PipelineDP - Write fast, flexible pipelines that use modern techniques to aggregate user data in a privacy-preserving manner.
• Compute Private Statistics with PipelineDP - This Google Developer Codelab walks through how to produce private statistics with differentially private aggregations using the PipelineDP Python framework.
• Practical Differential Privacy w/ Apache Beam - Blog post showing how to use Privacy on Beam from Google's differential privacy library.
• Computing Private Statistics with Privacy on Beam - This Google Developer Codelab walks through the use of Privacy on Beam to perform differentially private analysis in Go.
• Tumult Analytics - Tumult Analytics is a Python library for computing aggregate queries on tabular data using differential privacy.
• TensorFlow Federated - TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data.
• Differential Privacy Theory & Practice with Aaron Roth
• Differential Privacy at Bluecore with Zahi Karam
• Scalable Differential Privacy for Deep Learning with Nicolas Papernot
• Epsilon Software for Private Machine Learning with Chang Lu
• Privacy-Preserving Decentralized Data Science with Andrew Trask
• Sharing Data with Differential Privacy: A Primer
• Practitioners’ Guide to Accessing Emerging Differential Privacy Tools
• Evaluating Differential Privacy Tools’ Performance
• Getting Started with Scalable Differential Privacy Tools on the Cloud
• Privacy on Beam - A differential privacy framework built on top of Apache Beam.
• Stochastic Tester - Used to help catch regressions that could make the differential privacy property no longer hold.
• Differential Privacy Accounting Library - Used for tracking privacy budget.
• ZetaSQL Differential Privacy Extension - Command line interface for running differentially private SQL queries with ZetaSQL.
• DP-Auditorium - Used for auditing differential privacy guarantees.
• PySyft - PySyft is a Python library for secure and private Deep Learning.
• CrypTen - CrypTen is a framework for Privacy Preserving Machine Learning built on PyTorch.
• Opacus - A library that enables training PyTorch models with differential privacy.
• Uber SQL Differential Privacy - This repository contains a query analysis and rewriting framework to enforce differential privacy for general-purpose SQL queries. (deprecated)
• Google Differential Privacy Library - This repository contains libraries to generate ε- and (ε, δ)-differentially private statistics over datasets. Includes differential privacy "building block" libraries in C++, Go, and Java, as well as the following:
• PyDP - Python wrapper for Google's Differential Privacy project. The library provides a set of ε-differentially private algorithms, which can be used to produce aggregate statistics over numeric data sets containing private or sensitive information.
• IBM's Differential Privacy Library - Diffprivlib is a general-purpose library for experimenting with, investigating and developing applications in, differential privacy.
• RAPPOR - Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. (deprecated)
• FedML - FedML - The federated learning and distributed training library enabling machine learning anywhere at any scale. It's backed by FedML, Inc. Supporting large-scale geo-distributed training, cross-device federated learning on smartphones/IoTs, cross-silo federated learning on data silos, and research simulation. Best Paper Award at NeurIPS 2020.
• FedJAX - Google's JAX-based open source library for federated learning simulations that emphasizes ease-of-use in research.
• FLUTE - Created by Microsoft Research, Federated Learning Utilities and Tools for Experimentation (FLUTE) is a framework for running large-scale offline federated learning simulations.
• Federated Compute Platform - This Google repository hosts infrastructure for compiling and running federated programs and computations in the cross-device setting.
• TensorFlow Privacy - Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
• TensorFlow Encrypted - TF Encrypted is a framework for encrypted machine learning in TensorFlow.
• Differential Privacy at Bluecore with Zahi Karam
• Differential Privacy at the U.S. Census Bureau - Video on how differential privacy is being implemented in the U.S. Census.
• Uber SQL Differential Privacy - This repository contains a query analysis and rewriting framework to enforce differential privacy for general-purpose SQL queries. (deprecated)
• FedML - FedML - The federated learning and distributed training library enabling machine learning anywhere at any scale. It's backed by FedML, Inc. Supporting large-scale geo-distributed training, cross-device federated learning on smartphones/IoTs, cross-silo federated learning on data silos, and research simulation. Best Paper Award at NeurIPS 2020.
• Federated Compute Platform - This Google repository hosts infrastructure for compiling and running federated programs and computations in the cross-device setting.
• A Friendly, Non-Technical Introduction to Differential Privacy - Blog post that provides simple explanations for the core concepts behind differential privacy.
• A List of Real-World Uses of Differential Privacy - Blog post that compiles a list of real-world deployments of differential privacy, with their privacy parameters.
• Differential Privacy Theory & Practice with Aaron Roth
• Scalable Differential Privacy for Deep Learning with Nicolas Papernot
• Epsilon Software for Private Machine Learning with Chang Lu
• Privacy-Preserving Decentralized Data Science with Andrew Trask

Facial Recognition

• Understanding Facial Detection, Characterization and Recognition Technologies (Future of Privacy Forum (FPF) Infographic)
• LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition - Adversarial filter that accounts for the entire image processing pipeline and is demonstrably effective against industrial-grade pipelines that include face detection and large scale databases. Also includes an easy-to-use webtool that significantly degrades the accuracy of Amazon Rekognition and the Microsoft Azure Face Recognition API.
• Creating a Serverless Face Blurring Service for Photos in Amazon S3 - This blog post shows how to build a serverless face blurring service for photos uploaded to an Amazon S3 bucket.
• Fawkes - Fawkes, privacy preserving tool against facial recognition systems, developed by researchers at SANDLab, University of Chicago.
• Magritte - Google's Magritte is a MediaPipe-based library to redact faces from photos and videos. It provides processing graphs to reliably detect faces, track their movements in videos, and disguise the person's identity by obfuscating their face.

Homomorphic Encryption

• Building Safe A.I.: A Tutorial for Encrypted Deep Learning - Blogpost on how to train a neural network that is fully encrypted during training.
• nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data - Intel Research proposes an extension to its deep learning compiler to operate on homomorphically encrypted data.
• FHE.org - Community of researchers and developers interested in advancing Fully Homomorphic Encryption (FHE) and other secure computation techniques.
• HElib - HElib is an open-source software library that implements homomorphic encryption.
• Microsoft SEAL - Microsoft SEAL is an easy-to-use open-source (MIT licensed) homomorphic encryption library developed by the Cryptography and Privacy Research group at Microsoft.
• Google Fully-Homomorphic-Encryption - This repository created by Google contains open-source libraries and tools to perform fully homomorphic encryption operations on an encrypted data set.
• TFHE - The original version of TFHE (Fast Fully Homomorphic Encryption Library over the Torus) that implements the base arithmetic and functionalities (bootstrapped and leveled), allowing you to perform computations over encrypted data.
• Concrete - The concrete ecosystem is a set of crates (packages in the Rust language) that implements Zama's variant of TFHE, while most of the complexity of fully homomorphic encryption is hidden under high-level APIs.
• blyss - Open-source SDK for accessing data privately using homomorphic encryption.
• swift-homomorphic-encryption - Apple's open source Swift package that utilizes Private Information Retrieval (PIR).

Machine Learning and Algorithmic Bias

• Pribot and Polisis - Polisis is a unique way of visualizing privacy policies. Using deep learning, it allows you to know what the company is collecting about you, what it is sharing, etc.
• Ethical Machine Learning - Spotting and Preventing Proxy Bias - Jupyter Notebook from rOpenSciLabs that explores several ways of detecting unintentional bias and removing it from a predictive model.
• Fairness in Machine Learning Engineering - Google's Machine Learning Crash Course includes a 70-minute section on fairness.
• How to Incorporate Ethics and Risk into Your Machine Learning Development Process - To help highlight ethics and risk in machine learning, this article looks at the six steps involved in developing an ML system, what happens in each step, and the risk and ethics questions that arise.
• DrivenData: Deon - A command line tool to easily add an ethics checklist to your data science projects.
• Why Some Models Leak Data - Machine learning models use large amounts of data, some of which can be sensitive. If they're not trained correctly, sometimes that data is inadvertently revealed.
• Measuring Fairness - How do you make sure a model works equally well for different groups of people?
• How Randomized Response Can Help Collect Sensitive Information Responsibly - Giant datasets are revealing new patterns in cancer, income inequality and other important areas. However, the widespread availability of fast computers that can cross reference public data is making it harder to collect private information without inadvertently violating people's privacy. Modern randomization techniques can help preserve anonymity.
• Can a Model Be Differentially Private and Fair? - Training with differential privacy limits the information about any one data point that is extractable but in some cases there’s an unexpected side-effect: reduced accuracy with underrepresented subgroups disparately impacted.
• Hidden Bias - Models trained on real-world data can encode real-world bias. Hiding information about protected classes doesn't always fix things — sometimes it can even hurt.