2022/01/19 by Ni Ding, Ding, Ni · 1 citation
Computer Science · Mathematics · #Applications (stat.AP) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2201.07388
openalex publication_date 2022/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Pufferfish privacy achieves ε-indistinguishability over a set of secret pairs in the disclosed data. This paper studies how to attain ε-pufferfish privacy by exponential mechanism, an additive noise scheme that generalizes the Laplace noise. It is shown that the disclosed data is ε-pufferfish private if the noise is calibrated to the sensitivity of the Kantorovich optimal transport plan. Such a plan can be obtained directly from the data statistics conditioned on the secret, the prior knowledge of the system. The sufficient condition is further relaxed to reduce the noise power. It is also proved that the Gaussian mechanism based on the Kantorovich approach attains the δ-approximation of ε-pufferfish privacy.