6 citations · 15 across the 5 of their papers we have counts for
8 papers
Privacy Amplification via Iteration for Shuffled and Online PNSGD
Matteo Sordello, Zhiqi Bu, Jinshuo Dong
In this paper, we consider the framework of privacy amplification via iteration, which is originally proposed by Feldman et al. and subsequently simplified by Asoodeh et al. in the…
Rejoinder: Gaussian Differential Privacy
Jinshuo Dong, Aaron Roth, Weijie J. Su
In this rejoinder, we aim to address two broad issues that cover most comments made in the discussion. First, we discuss some theoretical aspects of our work and comment on how thi…
A Central Limit Theorem for Differentially Private Query Answering
Jinshuo Dong, Weijie J. Su, Linjun Zhang
Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The ce…
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng, Jinshuo Dong, Qi Long +1
Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall p…
Deep Learning with Gaussian Differential Privacy
Zhiqi Bu, Jinshuo Dong, Qi Long +1
Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increas…
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity
Jinshuo Dong, David Durfee, Ryan Rogers
Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consid…