その他 (国内) Privacy Amplification via Shuffled Check-Ins

Seng Pei Liew, Satoshi Hasegawa, Tsubasa Takahashi

コンピュータセキュリティシンポジウム2023 (CSS2023)


We study a protocol for distributed computation called shuffled check-in, which achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted shuffler. Leveraging differential privacy, we show that shuffled check-in achieves tight privacy guarantees through privacy am- plification, with a novel analysis based on R ́enyi differential privacy that improves privacy accounting over existing work. We also introduce a numerical approach to track the privacy of generic shuffling mechanisms, including Gaussian mechanism, which is the first evaluation of a generic mechanism under the distributed setting within the local/shuffle model in the literature. Empirical studies are also given to demonstrate the efficacy of the proposed approach.

Paper : Privacy Amplification via Shuffled Check-Ins新しいタブまたはウィンドウで開く (外部サイト)