Overview

Differential privacy studies algorithms whose outputs reveal limited information about any individual record, even when an attacker knows other data. Common approaches add calibrated noise to statistics or clipped gradients, use randomized response for local privacy, or combine privacy with subsampling, shuffling, and secure aggregation. These methods support private statistical estimation, machine learning, federated learning, and synthetic data generation, including for images, tables, and text; evaluation often measures both task utility and membership-inference risk. A central tension is that stronger guarantees can reduce accuracy or increase computational cost, while task-specific structure and tighter privacy accounting may improve the trade-off without resolving all deployment risks.

Latest papers

CardsList

281 papers43 papers in the last four weeks, up 54% on the four weeks before. 0.2% of all new papers.

  1. Quantum Information Ordering and Differential Privacy

    Nov 3, 2025Naqueeb Ahmad Warsi, Ayanava Dasgupta, Masahito HayashiDifferential Privacy

  2. Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station

    Oct 27, 2025Hao Liang, Haifeng Wen, Kaishun Wu +2Personalized Differential PrivacyOver-the-Air Federated Learning

  3. Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies

    Sep 13, 2025Yuping Wu, Viktor Schlegel, Warren Del-Pinto +11Medical Report GenerationHealthcare

  4. High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

    Aug 7, 2025Ziliang Shen, Caixing Wang, Shaoli Wang +1Quantile RegressionPrivacy-Preserving ML

  5. Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization

    Jun 4, 2025Hang Liu, Anna Scaglione, Sean PeisertPrivacy-Preserving MLGaussian Mixture Models

  6. Private Rate-Constrained Optimization with Applications to Fair Learning

    May 28, 2025Mohammad Yaghini, Tudor Cebere, Michael Menart +2Primal-Dual OptimizationGroup Fairness

  7. A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input

    May 20, 2025Bar Mahpud, Or SheffetDifferential Privacy

  8. Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model

    Feb 8, 2025Zinan Lin, Tadas Baltrusaitis, Wenyu Wang +1Differentially Private Synthetic Data GenerationSynthetic Data Generation

  9. Noise-Aware Differentially Private Variational Inference

    Oct 25, 2024Talal Alrawajfeh, Joonas Jälkö, Antti HonkelaBayesian InferenceDifferential Privacy

  10. Differentially Private Covariate Balancing Causal Inference

    Oct 18, 2024Yuki Ohnishi, Jordan AwanCausal Effect EstimationCausal Inference

  11. DNA: Differentially private Neural Augmentation for contact tracing

    Apr 20, 2024Rob Romijnders, Christos Louizos, Yuki M. Asano +1Privacy-Preserving MLDifferential Privacy

  12. Protect Your Score: Contact Tracing With Differential Privacy Guarantees

    Dec 18, 2023Rob Romijnders, Christos Louizos, Yuki M. Asano +1Differential PrivacyPrivacy-Utility Tradeoff

  13. Label Differential Privacy via Aggregation

    Oct 16, 2023Anand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3Privacy-Preserving MLDifferential Privacy

  14. A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning

    Jul 24, 2023Spencer Giddens, Yiwang Zhou, Kevin R. Krull +3Privacy-Preserving MLDifferential Privacy