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1407.2674
Cited By
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
International Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2013
10 July 2014
A. Beimel
Kobbi Nissim
Uri Stemmer
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Papers citing
"Private Learning and Sanitization: Pure vs. Approximate Differential Privacy"
50 / 114 papers shown
Nearly-Optimal Private Selection via Gaussian Mechanism
Ethan Leeman
Pasin Manurangsi
MLT
92
0
0
10 Nov 2025
Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
B. Li
Wei Wang
Peng Ye
308
1
0
01 Oct 2025
Private Learning of Littlestone Classes, Revisited
Xin Lyu
201
3
0
30 Sep 2025
Privacy-Preserving Conformal Prediction Under Local Differential Privacy
International Symposium on Conformal and Probabilistic Prediction with Applications (ISCPPA), 2025
Coby Penso
Bar Mahpud
Jacob Goldberger
Or Sheffet
438
4
0
21 May 2025
An
O
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O
~
ptimal Differentially Private Learner for Concept Classes with VC Dimension 1
Chao Yan
347
0
0
10 May 2025
Differentially Private Substring and Document Counting with Near-Optimal Error
Giulia Bernardini
Philip Bille
Inge Li Gørtz
Teresa Anna Steiner
311
1
0
18 Dec 2024
Differentially Private Multi-Sampling from Distributions
International Conference on Algorithmic Learning Theory (ALT), 2024
Albert Cheu
Debanuj Nayak
271
2
0
13 Dec 2024
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Maryam Aliakbarpour
Syomantak Chaudhuri
T. Courtade
Alireza Fallah
Michael I. Jordan
310
1
0
24 Oct 2024
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Falguni Roy
Xiaofeng Ding
K. -K. R. Choo
Pan Zhou
FaML
239
2
0
19 Sep 2024
Credit Attribution and Stable Compression
Roi Livni
Shay Moran
Kobbi Nissim
Chirag Pabbaraju
259
2
0
22 Jun 2024
On the Exponential Convergence for Offline RLHF with Pairwise Comparisons
Zhirui Chen
Vincent Y. F. Tan
OffRL
288
1
0
18 Jun 2024
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Rudrajit Das
Inderjit S Dhillon
Alessandro Epasto
Adel Javanmard
Jieming Mao
Vahab Mirrokni
Sujay Sanghavi
Peilin Zhong
525
3
0
17 Jun 2024
Private Geometric Median
Mahdi Haghifam
Thomas Steinke
Jonathan R. Ullman
231
2
0
11 Jun 2024
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
Krishnamurthy Dvijotham
H. B. McMahan
Krishna Pillutla
Thomas Steinke
Abhradeep Thakurta
408
28
0
25 Apr 2024
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna
Edward Raff
Nathan Inkawhich
277
5
0
01 Apr 2024
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah
Michael Menart
Raef Bassily
Cristóbal Guzmán
Raman Arora
412
3
0
06 Mar 2024
On the Growth of Mistakes in Differentially Private Online Learning: A Lower Bound Perspective
Daniil Dmitriev
Kristóf Szabó
Amartya Sanyal
207
5
0
26 Feb 2024
Private PAC Learning May be Harder than Online Learning
Mark Bun
Aloni Cohen
Rathin Desai
223
3
0
16 Feb 2024
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Neural Information Processing Systems (NeurIPS), 2023
Ashwinkumar Badanidiyuru
Badih Ghazi
Pritish Kamath
Ravi Kumar
Ethan Leeman
Pasin Manurangsi
A. Varadarajan
Chiyuan Zhang
441
8
0
09 Dec 2023
Local differential privacy in survival analysis using private failure indicators
Electronic Journal of Statistics (EJS), 2023
Egea Maxime
Escobar-Bach Mikael
433
0
0
02 Nov 2023
Differentially Private Reward Estimation with Preference Feedback
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Sayak Ray Chowdhury
Xingyu Zhou
Nagarajan Natarajan
374
10
0
30 Oct 2023
Using Participants' Utility Functions to Compare Versions of Differential Privacy
Nitin Kohli
Michael Carl Tschantz
247
0
0
10 Oct 2023
Private Distribution Learning with Public Data: The View from Sample Compression
Neural Information Processing Systems (NeurIPS), 2023
Shai Ben-David
Alex Bie
C. Canonne
Gautam Kamath
Vikrant Singhal
363
17
0
11 Aug 2023
Eliminating Label Leakage in Tree-Based Vertical Federated Learning
Hideaki Takahashi
Qingbin Liu
Yang Liu
AAML
FedML
345
7
0
19 Jul 2023
Differentially Private Domain Adaptation with Theoretical Guarantees
International Conference on Machine Learning (ICML), 2023
Raef Bassily
Corinna Cortes
Anqi Mao
M. Mohri
318
0
0
15 Jun 2023
PILLAR: How to make semi-private learning more effective
Francesco Pinto
Yaxian Hu
Fanny Yang
Amartya Sanyal
264
14
0
06 Jun 2023
Differentially Private Medians and Interior Points for Non-Pathological Data
Information Technology Convergence and Services (ITCS), 2023
Maryam Aliakbarpour
Rose Silver
Thomas Steinke
Jonathan R. Ullman
189
3
0
22 May 2023
Private Everlasting Prediction
Neural Information Processing Systems (NeurIPS), 2023
M. Naor
Kobbi Nissim
Uri Stemmer
Chao Yan
322
5
0
16 May 2023
Learning from Aggregated Data: Curated Bags versus Random Bags
Lin Chen
Gang Fu
Amin Karbasi
Vahab Mirrokni
FedML
254
12
0
16 May 2023
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization
Symposium on the Theory of Computing (STOC), 2023
Mark Bun
Marco Gaboardi
Max Hopkins
R. Impagliazzo
Rex Lei
T. Pitassi
Satchit Sivakumar
Jessica Sorrell
283
46
0
22 Mar 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Journal of Artificial Intelligence Research (JAIR), 2023
Natalia Ponomareva
Hussein Hazimeh
Alexey Kurakin
Zheng Xu
Carson E. Denison
H. B. McMahan
Sergei Vassilvitskii
Steve Chien
Abhradeep Thakurta
597
263
0
01 Mar 2023
Do PAC-Learners Learn the Marginal Distribution?
International Conference on Algorithmic Learning Theory (ALT), 2023
Max Hopkins
D. Kane
Shachar Lovett
G. Mahajan
663
4
0
13 Feb 2023
Pushing the Boundaries of Private, Large-Scale Query Answering
Brendan Avent
Aleksandra Korolova
210
0
0
09 Feb 2023
Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model
Embedded Systems and Applications (ESA), 2023
Menachem Sadigurschi
M. Shechner
Uri Stemmer
AAML
240
0
0
22 Jan 2023
Regression with Label Differential Privacy
International Conference on Learning Representations (ICLR), 2022
Badih Ghazi
Pritish Kamath
Ravi Kumar
Ethan Leeman
Pasin Manurangsi
A. Varadarajan
Chiyuan Zhang
486
22
0
12 Dec 2022
Differentially-Private Bayes Consistency
Olivier Bousquet
Haim Kaplan
A. Kontorovich
Yishay Mansour
Shay Moran
Menachem Sadigurschi
Uri Stemmer
264
0
0
08 Dec 2022
Answering Private Linear Queries Adaptively using the Common Mechanism
Proceedings of the VLDB Endowment (PVLDB), 2022
Yingtai Xiao
Guanhong Wang
Qiang Yan
Daniel Kifer
312
9
0
30 Nov 2022
The Bounded Gaussian Mechanism for Differential Privacy
Journal of Privacy and Confidentiality (JPC), 2022
Bo Chen
Matthew T. Hale
229
10
0
30 Nov 2022
Generalized Private Selection and Testing with High Confidence
Information Technology Convergence and Services (ITCS), 2022
E. Cohen
Xin Lyu
Jelani Nelson
Tamas Sarlos
Uri Stemmer
369
7
0
22 Nov 2022
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization
Symposium on the Theory of Computing (STOC), 2022
E. Cohen
Xin Lyu
Jelani Nelson
Tamas Sarlos
Uri Stemmer
187
23
0
11 Nov 2022
Privacy-Preserving Models for Legal Natural Language Processing
Ying Yin
Ivan Habernal
PILM
AILaw
200
12
0
05 Nov 2022
Private Isotonic Regression
Neural Information Processing Systems (NeurIPS), 2022
Badih Ghazi
Pritish Kamath
Ravi Kumar
Pasin Manurangsi
263
0
0
27 Oct 2022
Algorithms with More Granular Differential Privacy Guarantees
Information Technology Convergence and Services (ITCS), 2022
Badih Ghazi
Ravi Kumar
Pasin Manurangsi
Thomas Steinke
329
8
0
08 Sep 2022
Private Estimation with Public Data
Neural Information Processing Systems (NeurIPS), 2022
Alex Bie
Gautam Kamath
Vikrant Singhal
353
38
0
16 Aug 2022
Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions
Neural Information Processing Systems (NeurIPS), 2022
Omri Ben-Eliezer
Dan Mikulincer
Ilias Zadik
FedML
342
8
0
15 Aug 2022
Private Domain Adaptation from a Public Source
Raef Bassily
M. Mohri
A. Suresh
172
4
0
12 Aug 2022
How unfair is private learning ?
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Amartya Sanyal
Yaxian Hu
Fanny Yang
FaML
FedML
454
27
0
08 Jun 2022
Private Convex Optimization via Exponential Mechanism
Annual Conference Computational Learning Theory (COLT), 2022
Sivakanth Gopi
Y. Lee
Daogao Liu
442
60
0
01 Mar 2022
On the Robustness of CountSketch to Adaptive Inputs
International Conference on Machine Learning (ICML), 2022
E. Cohen
Xin Lyu
Jelani Nelson
Tamas Sarlos
M. Shechner
Uri Stemmer
AAML
232
29
0
28 Feb 2022
Differentially Private Top-k Selection via Canonical Lipschitz Mechanism
Michael Shekelyan
Grigorios Loukides
229
4
0
31 Jan 2022
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