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The Right to Be Forgotten under Correlation: Privacy-Utility Tradeoffs

Year: 2026

Type: preprint

Abstract: We study privacy-preserving algorithms that redact information from a correlated dataset. The goal is to reduce the information leakage about sensitive parts. We analytically characterize the optimal tradeoff between privacy (leakage through correlation) and utility (number of redactions) for local mechanisms and binary Markovian correlations. Our result shows how to decompose the mechanism design problem into a sequence of local mechanism design problems and reveals their sequential relation. The optimal mechanism is the solution to a Finite-Horizon Deterministic Markov Decision Process.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.17088

Authors: Massny, Luis, Bitar, Rawad, Ye, Fangwei, Rouayheb, Salim El

Institutions: Technical University of Munich, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Award: G7339589892

OpenAlex ID: W7221801625

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