Resources
Searching for statistical data privacy resources? You’re in the right place. Here we provide a range of resources, from CRAN packages to influential articles.
Articles
- A Statistical Framework for Differential Privacy (2012) by Larry Wasserman and Shuheng Zhou
- Advances and Open Problems in Federated Learning (2021) by Peter Kairouz et al.
- Balancing Data Privacy and Usability in the Federal Statistical System (2022) by V. Joseph Hotz et al.
- Disclosure Control and Random Tabular Adjustment (2018) by Mark Stinner
- Federated Learning: Challenges, Methods, and Future Directions (2020) by Tian Li et al.
- How Statisticians Should Grapple with Privacy in a Changing Data Landscape (2020) by Joshua Snoke and Claire McKay Bowen
- Policy Impacts of Statistical Uncertainty and Privacy (2022) by Ryan Steed et al.
- Privacy-Preserving Data Analysis for the Federal Statistical Agencies (2017) by John Abowd et al.
- Statistical Data Privacy: A Song of Privacy and Utility (2023) by Aleksandra Slavković and Jeremy Seeman
Books and Book Chapters
- Statistical Disclosure Control for Survey Data (2009) by Chris Skinner
- Protecting Your Privacy in a Data-Driven World (2021) by Claire McKay Bowen
CRAN (R) Packages
- acro: A Tool for Automating the Statistical Disclosure Control of Research Outputs
- AQuadtree: Confidentiality of Spatial Point Data
- cellKey: Consistent Perturbation of Statistical Frequency and Magnitude Tables
- diffpriv: Easy Differential Privacy
- duawranglr: Securely Wrangle Dataset According to Data Usage Agreement
- easySdcTable: Easy Interface to the Statistical Disclosure Control Package ‘sdcTable’ Extended with Own Implementation of ‘GaussSuppression’
- pda: Privacy-Preserving Distributed Algorithms
- ppmf: Read Census Privacy Protected Microdata Files
- ppmHR: Privacy-Protecting Hazard Ratio Estimation in Distributed Data Networks
- PPRL: Privacy Preserving Record Linkage
- RegSDC: Information Preserving Regression-Based Tools for Statistical Disclosure Control
- sdcHierarchies: Create and (Interactively) Modify Nested Hierarchies
- sdcLog: Tools for Statistical Disclosure Control in Research Data Centers
- SDCNWay: Tools to Evaluate Disclosure Risk
- sdcSpatial: Statistical Disclosure Control for Spatial Data
- sdcTable: Methods for Statistical Disclosure Control in Tabular Data
- synthpop: Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control
- uwedragon: Data Research, Access, Governance Network : Statistical Disclosure Control
Python Packages
- differential-privacy: Google’s Differential Privacy Libraries
- diffprivlib: General-Purpose Library for Experimenting with, Investigating, and Developing Applications in, Differential Privacy
- opendp: The Core Library of Differential Privacy Algorithms Powering the OpenDP Project
- pipeline-dp: Python Framework for Applying Differentially Private Aggregations to Large Datasets Using Batch Processing Systems such as Apache Spark, Apache Beam, and More
- pysyft: Perform Data Science on Data that Remains in Someone Else’s Server
- python-dp: OpenMined’s Python Differential Privacy Library
- tensorflow-privacy: Library for Training Machine Learning Models with Privacy for Training Data
Additional Resources
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