22 citations · 64 across the 12 of their papers we have counts for
12 papers
Error-Tolerant E-Discovery Protocols
Jinshuo Dong, Jason D. Hartline, Liren Shan +1
We consider the multi-party classification problem introduced by Dong, Hartline, and Vijayaraghavan (2022) in the context of electronic discovery (e-discovery). Based on a request…
Classification Protocols with Minimal Disclosure
Jinshuo Dong, Jason Hartline, Aravindan Vijayaraghavan
We consider multi-party protocols for classification that are motivated by applications such as e-discovery in court proceedings. We identify a protocol that guarantees that the re…
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
Jordan Awan, Jinshuo Dong
A canonical noise distribution (CND) is an additive mechanism designed to satisfy -differential privacy (-DP), without any wasted privacy budget. -DP is a hypothesis testi…
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…
Optimal Accounting of Differential Privacy via Characteristic Function
Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang
Characterizing the privacy degradation over compositions, i.e., privacy accounting, is a fundamental topic in differential privacy (DP) with many applications to differentially pri…
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…
Equilibrium Characterization for Data Acquisition Games
Jinshuo Dong, Hadi Elzayn, Shahin Jabbari +2
We study a game between two firms in which each provide a service based on machine learning. The firms are presented with the opportunity to purchase a new corpus of data, which wi…
Strategic Classification from Revealed Preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman +2
We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome…