PROTEUS: A Practical and Rigorous Toolkit for Privacy
Statistical privacy, or the problem of disclosing aggregate statistics about data collected from individuals while ensuring the privacy of individual level sensitive properties, is an important problem in today’s age of big data. The key challenge in statistical privacy is that applications for data collection and analysis operate on varied kinds of data, and have diverse requirements for the information that must be kept secret, and the adversaries that they must tolerate. Thus, application domain experts, who are frequently not experts in privacy, cannot directly use an existing, general-purpose privacy definition. Instead, they must develop a new privacy definition or customize an existing one. Currently there exist no rigorous techniques to customize privacy to applications.
This project builds PROTEUS, a general-purpose toolkit for developing rigorous privacy definitions and mechanisms that can be customized to applications. The cornerstone of PROTEUS is a novel privacy framework that allows customized privacy protection by explicitly listing the secrets to be protected, enumerating the (potentially infinite set of) possible adversaries, and ensuring rigorous bounds on the information disclosed to each adversary about every secret. Novel theoretical tools in PROTEUS include methods to reason about privacy for correlated data, privacy against realistic adversaries, and techniques to express and enforce personalized privacy preferences. These tools result in practical privacy mechanisms for publishing social science survey data, social network analysis, and analysis of user-activity streams. Broader impacts of this project include developing new courses in privacy and big-data management, as well as technology transfer to the US Census.
- Faculty: Ashwin Machanavajjhala (CS)
- “Design of Policy-Aware Differentially Private Algorithms”, Sam Haney, Ashwin Machanavajjhala, and Bolin Ding accepted at VLDB 2016 vol 9.
- “Designing Statistical Privacy for your Data“, Ashwin Machanavajjhala, Dan Kifer, Communications of the ACM (Mar 2015)
- “Formal privacy protection for data products combining individual and employer frames”, Samuel Haney, Ashwin Machanavajjhala, Mark Kutzbach, Matthew Graham, John Abowd, Lars Vilhuber, UNECE/Eurostat Statistical Data Confidentiality Work Session Oct 2015
- “Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies”, Xi He, Ashwin Machanavajjhala, and Bolin Ding accepted at SIGMOD 2014
- “Pufferfish: A framework for mathematical privacy definitions“, Daniel Kifer, Ashwin Machanavajjhala, ACM TODS 39(1), 2014
- “A rigorous and customizable framework for privacy“, Daniel Kifer, Ashwin Machanavajjhala, ACM PODS, 2012
- “No Free Lunch in Data Privacy“, Daniel Kifer, Ashwin Machanavajjhala, ACM SIGMOD, 2011