5 papers
-Wasserstein Mechanism for Rényi Pufferfish Privacy
Ni Ding, Wenjin Yang, Zijian Zhang
This paper introduces the -Wasserstein mechanism for achieving Rényi Pufferfish Privacy using Laplace and Gaussian noise. By leveraging Hölder's inequality, we demonstrate th…
Rényi Pufferfish Privacy with Gaussian-based Priors: From Single Gaussian to Mixture Model
Wenjin Yang, Ni Ding, Zijian Zhang +5
Rényi Pufferfish Privacy (RPP) provides a Rényi divergence-based privacy framework for correlated data, but existing -Wasserstein mechanisms are often conservative and sa…
Multi-user Pufferfish Privacy
Ni Ding, Songpei Lu, Wenjing Yang +1
This paper studies how to achieve individual indistinguishability by pufferfish privacy in aggregated query to a multi-user system. It is assumed that each user reports realization…
EByFTVeS: Efficient Byzantine Fault Tolerant-based Verifiable Secret-sharing in Distributed Privacy-preserving Machine Learning
Zhen Li, Zijian Zhang, Wenjin Yang +5
Verifiable Secret Sharing (VSS) has been widespread in Distributed Privacy-preserving Machine Learning (DPML), because invalid shares from malicious dealers or participants can be…
Noise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method
Wenjin Yang, Ni Ding, Zijian Zhang +8
This paper introduces a relaxed noise calibration method to enhance data utility while attaining pufferfish privacy. This work builds on the existing -Wasserstein (Kantorovich)…