What Can We Learn Privately?

What Can We Learn Privately?

6 March 2008

S. Kasiviswanathan

Sofya Raskhodnikova

Papers citing "What Can We Learn Privately?"

19 / 19 papers shown

Title
Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy Haoqi Wu Wei Dai Li Wang Qiang Yan SILM 92 1 0 09 May 2025
Federated Heavy Hitter Analytics with Local Differential Privacy Yuemin Zhang Qingqing Ye Haibo Hu FedML 206 1 0 03 Jan 2025
Segmented Private Data Aggregation in the Multi-message Shuffle Model Shaowei Wang Hongqiao Chen Sufen Zeng Ruilin Yang Hui Jiang ... Kaiqi Yu Rundong Mei Shaozheng Huang Wei Yang Bangzhou Xin FedML 100 0 0 31 Dec 2024
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 123 0 0 30 Nov 2024
Near Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo Arun Ganesh Saminul Haque Thomas Steinke Abhradeep Thakurta 108 9 0 08 Oct 2024
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy Bo Li Wei Wang Peng Ye FedML 64 0 0 30 Jul 2024
Contraction of Private Quantum Channels and Private Quantum Hypothesis Testing Theshani Nuradha Mark M. Wilde 63 7 0 26 Jun 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling Shaowei Wang Changyu Dong Xiangfu Song Jin Li Zhili Zhou Di Wang Han Wu 91 0 0 26 Jun 2024
NAP^2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human Shuo Huang William MacLean Xiaoxi Kang Qiongkai Xu Zhuang Li Xingliang Yuan Zhuang Li Lizhen Qu 90 0 0 06 Jun 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 77 10 0 23 May 2024
On Lattices, Learning with Errors, Random Linear Codes, and Cryptography O. Regev LRM 105 1,079 0 08 Jan 2024
DP-SGD with weight clipping Antoine Barczewski Jan Ramon 90 1 0 27 Oct 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 123 4 0 01 Aug 2023
Personalized Privacy Amplification via Importance Sampling Dominik Fay Sebastian Mair Jens Sjölund 89 0 0 05 Jul 2023
Almost-everywhere algorithmic stability and generalization error S. Kutin P. Niyogi 94 173 0 12 Dec 2012
A Learning Theory Approach to Non-Interactive Database Privacy Avrim Blum Katrina Ligett Aaron Roth 84 550 0 10 Sep 2011
Differential Privacy with Compression Shuheng Zhou Katrina Ligett Larry A. Wasserman 145 65 0 10 Jan 2009
A statistical framework for differential privacy Larry A. Wasserman Shuheng Zhou 102 485 0 16 Nov 2008
Efficient, Differentially Private Point Estimators Adam D. Smith FedML 71 78 0 27 Sep 2008