Tetsuya Sato · 2026 · arXiv
DOI: 10.48550/arXiv.2610.25636 · open-access PDF
差別プライバシー(DP)は、データベースのプライバシー保証を評価するために設計されたプライバシーのゴールドスタンダード定義です。 Rényi差別プライバシー(RDP)は、機械学習アルゴリズムの厳密な評価を提供するDPの重要なバージョンです。これらの公式は理解しやすいが、実際のデータベースのプライバシーの評価は複雑です。データベースの小さな変更でさえ、プライバシーを破ることができ、DPとRDPの評価自体は複雑です。特に、最近の研究におけるRDPの評価はより難しくなっています。
Machine translation (Meta M2M100); the English original follows.
Differential privacy (DP) is a gold-standard definition of privacy designed to evaluate the privacy guarantee of databases. Rényi differential privacy (RDP) is an important variant of DP that gives tight evaluations of machine learning algorithms. These formulations are easy to understand, but evaluating privacy for actual databases is complicated. Even a small change in the database can break its privacy, and evaluations of DP and RDP themselves are complicated. In particular, RDP evaluations in recent studies have become much harder. Therefore, formal verification of them has been studied fo
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