Differential Privacy

Ask about this topic

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

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

    State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering, University of Macau, Macao S.A.R. · Department of Electrical and Computer Engineering, Cornell Tech, Cornell University, New York, NY, 10044 USA. · Computing Sciences Research, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA.

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

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

    University of Toronto · Vector Institute · PreMeDICaL team +3

  3. Differential Privacy of Decentralized Learning: Centralized Rate Conditions and Nearly Tight Guarantees

    May 26, 2025Antti Koskela, Tejas KulkarniDifferential PrivacyDecentralized Learning

    Nokia Bell Labs

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

    May 20, 2025Bar Mahpud, Or SheffetDifferential Privacy

    Faculty of Engineering Bar-Ilan University Israel

  5. 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

    Microsoft Research Redmond, WA, USA · Microsoft Cambridge, UK · Redmond, WA, USA

  6. Noise-Aware Differentially Private Variational Inference

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

    University of Helsinki

  7. Differentially Private Covariate Balancing Causal Inference

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

    Department of Biostatistics, Yale School of Public Health · Department of Statistics, University of Pittsburgh

  8. DNA: Differentially private Neural Augmentation for contact tracing

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

    University of Amsterdam · Qualcomm AI research

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

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

    University of Amsterdam · Qualcomm AI research

  10. Label Differential Privacy via Aggregation

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

    Google Research India.

  11. 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

    University of Notre Dame, Notre Dame, IN 46556, USA · St. Jude Children’s Research Hospital, Memphis, TN 38138, USA · University of Michigan, Ann Arbor, MI 48109, USA