2021/07/13 by March T. Boedihardjo, Thomas Strohmer, Boedihardjo, March +3 · 1 citation
Computer Science · Decision Sciences · Social Sciences · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Mathematics #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #Probability (math.PR) #Probability and Risk Models #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2107.05824
openalex publication_date 2021/07/13 · openalex created_date 2022/08/12 · openalex updated_date 2026/07/28
The protection of private information is of vital importance in data-driven research, business, and government. The conflict between privacy and utility has triggered intensive research in the computer science and statistics communities, who have developed a variety of methods for privacy-preserving data release. Among the main concepts that have emerged are anonymity and differential privacy. Today, another solution is gaining traction, synthetic data. However, the road to privacy is paved with NP-hard problems. In this paper we focus on the NP-hard challenge to develop a synthetic data generation method that is computationally efficient, comes with provable privacy guarantees, and rigorously quantifies data utility. We solve a relaxed version of this problem by studying a fundamental, but a first glance completely unrelated, problem in probability concerning the concept of covariance loss. Namely, we find a nearly optimal and constructive answer to the question how much information is lost when we take conditional expectation. Surprisingly, this excursion into theoretical probability produces mathematical techniques that allow us to derive constructive, approximately optimal solutions to difficult applied problems concerning microaggregation, privacy, and synthetic data.