Differential Privacy

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30 papers in the last four weeks, up 100% on the four weeks before. 0.2% of all new papers.

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Private Learning with Public Feature Conditioning
Jun 17, 2026Shuli Jiang, Walid Krichene, Nicolas MayorazDifferentially Private Stochastic Gradient Descent Privacy-Preserving ML
1AWS Agentic AI · 2Microsoft · 3Google Research
Differential Privacy of Gaussian Process Posterior Sampling
Jun 16, 2026Tomasz MaciazekGaussian Processes Privacy-Preserving ML
School of Mathematics, University of Bristol
Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning
Jun 15, 2026Xiaolin Li, Ning Wang, Ninghui Li +1Backdoor Attacks in Federated Learning Differentially Private Federated Learning
Purdue University West Lafayette, IN, USA · University of South Florida Tampa, FL, USA
Cross-Silo De-Anonymization Under Local Differential Privacy: Threat Model, Phase Transition, and Coordination Necessity
Jun 15, 2026Ziniu Liu, Aiping LiDifferential Privacy
National University of Defense Technology
Causal-Privacy Audit Workflow for Synthetic and Distilled Data in Dropout Support
Jun 14, 2026Hanghang Zheng, Xiwei Zhuang, Zhong Wang +4Privacy Auditing Causal Effect Estimation
Central University of Finance and Economics, Beijing, PR China · China Development Bank, Beijing, PR China
Let's Ask Gauss: Improved One-Run Privacy Auditing
Jun 10, 2026Adya Agrawal, Yu Wei, Jaspal Singh +2Privacy Auditing Differentially Private Stochastic Gradient Descent
Georgia Institute of Technology · Purdue University · Rensselaer Polytechnic Institute
Computationally tractable robust differentially private mean estimation
Jun 10, 2026Kelly RamsayDifferential Privacy Data Contamination
Department of Mathematics and Statistics York University Toronto, ON, Canada
Fixed-Parameter Tractability of Private Synthetic Data Generation
Jun 9, 2026Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3Synthetic Data Generation Privacy-Preserving ML
Google Deepmind · Institute for Mathematical and Computational Engineering, Faculty of Mathematics and School of Engineering, Pontificia Universidad Cat´olica de Chile · Google Research
On Choosing the $μ$ Parameter in Gaussian Differential Privacy
Jun 8, 2026Bogdan Kulynych, Antti HonkelaDifferential Privacy Gaussian Differential Privacy
Biomedical Data Science Center, Lausanne University Hospital · University of Helsinki
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
Jun 8, 2026Bartłomiej Marek, Lorenzo Rossi, Vincent Hanke +4Privacy-Preserving Language Models Membership Inference Attacks
CISPA Helmholtz Center for Information Security
Differentially Private Synthetic Data via APIs 4: Tabular Data
Jun 6, 2026Toan Tran, Arturs Backurs, Zinan Lin +3Synthetic Data Generation Synthetic Tabular Data Generation
Emory University · Microsoft Research
Differentially Private Range Subgraph Counting
Jun 6, 2026Xian Chen, Ruobing Bai, Pan PengDifferential Privacy
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China.
DP-MacAdam: Differentially Private Mechanism with Adaptive Clipping and Adaptive Momentum
Jun 3, 2026Naima Tasnim, Lalitha Sankar, Oliver KosutDifferentially Private Stochastic Gradient Descent Gradient Clipping
Arizona State University, Tempe, AZ, United States
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
Jun 3, 2026Yunsheng Yuan, Xue Xiao, Lina Wang +1Decentralized Learning Privacy-Preserving ML
School of Computer Science and Technology, Shandong University, Qingdao 266237, China · Inspur Cloud Information Technology Co, Ltd.
Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD
Jun 3, 2026Xiaobo Huang, Fang XieDifferentially Private Stochastic Gradient Descent Differential Privacy
Guangdong Provincial Key Laboratory of IRADS, Beijing · Normal-Hong Kong Baptist University · Zhuhai, China
When Do Fewer Coordinates Suffice in DP-SGD?
Jun 3, 2026Huiqi Zhang, Fang XieDifferentially Private Stochastic Gradient Descent Nonconvex Stochastic Optimization
Guangdong Provincial Key Laboratory of IRADS, Beijing Normal-Hong Kong Baptist University Zhuhai, China
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Jun 1, 2026Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella SunFederated Learning Aggregation Privacy-Preserving ML
University of Tennessee, Knoxville, USA · Oak Ridge National Laboratory, USA
Randomized Least Squares Value Iteration itself is Joint Differentially Private
Jun 1, 2026Haiyang Lu, Pratik Gajane, Shaojie Bai +1Markov Decision Processes RL Exploration
1Laboratoire d’Informatique Fondamentale d’Orléans (LIFO), Université d’Orléans · College of Control Science and Engineering, Zhejiang University · Department of Computer Science, University of Copenhagen
Private and Stable Test-Time Adaptation with Differential Privacy
Jun 1, 2026Zefeng Li, Qiaoyue Tang, Mathias Lecuyer +1Continual Test-Time Adaptation Privacy-Preserving ML
University of British Columbia · Vector Institute
ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
Jun 1, 2026Peihan Liu, Lucas Rosenblatt, Weiwei Kong +7Privacy-Preserving Language Models Synthetic Benchmark Generation
Columbia University · Work done as a Student Researcher at Google · NYU +4
Differentially Private Datastore Generation for Retrieval-Augmented Inference
May 31, 2026Abdelrahman Abouelenein, Marwan TorkiPrivacy-Preserving ML Differential Privacy
Department of Computer and Systems Engineering Alexandria University, Egypt · Microsoft
PRISM: Gauge-Invariant Tangent-Space Differentially Private LoRA
May 31, 2026Shihao Wang, Xueru ZhangDifferentially Private Stochastic Gradient Descent Low-Rank Adaptation
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
PE-means: Improved Differentially Private $k$ -means Clustering through Private Evolution
May 29, 2026Thomas Humphries, Zinan Lin, Sergey YekhaninClustering Privacy-Preserving ML
University of Waterloo · Microsoft Research
The Fast Mixing Mechanism for Differential Privacy
May 28, 2026Omri Lev, Moshe Shenfeld, Vishwak Srinivasan +2Randomized Sketching Differential Privacy
Massachusetts Institute of Technology · The Hebrew University of Jerusalem
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
May 28, 2026Madhura Pathegama, Srikanth Avasarala, Viveck R. Cadambe +1Differential Privacy
Georgia Institute of Technology, Atlanta, GA, USA
Optimal Gap-Dependent Regret for Private Stochastic Decision-Theoretic Online Learning
May 27, 2026Tommaso Cesari, Roberto ColomboniDifferential Privacy Online Learning
School of Electrical Engineering and Computer Science University of Ottawa Ottawa, Canada · School of Mathematics University of Bristol Bristol, United Kingdom
Optimal Rates for Differentially Private Hypothesis Testing with E-values
May 27, 2026Ben Jacobsen, Tomas Gonzalez, Gavin Brown +2E-Values Differential Privacy
University of Wisconsin-Madison · Carnegie Mellon University · University of Wisconsin–Madison
Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
May 27, 2026Huikang Liu, Aras Selvi, Wolfram WiesemannDifferential Privacy
1Shanghai Jiao Tong University · 2UCL School of Management · 3Imperial Business School
Privately Estimating Monotone Statistics in Polynomial Time
May 27, 2026Gavin Brown, Ephraim Linder, Mahbod Majid +1Sample Complexity Differential Privacy
University of Wisconsin–Madison · Boston University · MIT Mathematics +1
Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
May 27, 2026Yvonne Zhou, Mingyu Liang, Ivan Brugere +5Differentially Private Stochastic Gradient Descent Homomorphic Encryption
University of Maryland, College Park, MD 20742 · J.P. Morgan AI Research, New York, NY, 10017 · AlgoCRYPT CoE

Jun 17, 2026, cs.LGJ/K move · Enter open · S save

Private Learning with Public Feature Conditioning

Shuli Jiang, Walid Krichene, Nicolas Mayoraz

1AWS Agentic AI · 2Microsoft · 3Google Research

Differentially Private Stochastic Gradient Descent Privacy-Preserving ML Differential Privacy

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We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and advertising systems. While such label-DP or semi-sensitive-feature settings have been primarily explored in the context of classification, effective approaches for regression remain underexplored. We introduce Cond-DP, a conditioned variant of DPSGD that leverages the structure of public feature matrices to improve optimization under privacy constraints. Motivated by the observation that these public features often exhibit rapidly decaying spectra, Cond-DP incorporates a data-driven conditioning matrix to reshape the optimization landscape and accelerate convergence. We provide convergence guarantees for convex, strongly convex, and non-convex settings, and recover standard DPSGD as a special case when the conditioning matrix is the identity. We show how to construct an effective conditioning matrix for Cond-DP directly from public features, enabling provably faster convergence than DPSGD in private linear regression without incurring additional privacy cost. Empirically, Cond-DP with this conditioning matrix consistently outperforms state-of-the-art baselines across a wide range of datasets and model architectures under label DP, demonstrating strong and robust performance in practice.