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0803.0924
Cited By
What Can We Learn Privately?
6 March 2008
S. Kasiviswanathan
Homin K. Lee
Kobbi Nissim
Sofya Raskhodnikova
Adam D. Smith
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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
201
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
105
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
87
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
102
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
120
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
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