NeurIPS 2023: DP Papers

Anonymous

what are the papers discussing differential privacy ?

Chat NeurIPS logo
Chat NeurIPS

The search for papers discussing differential privacy at NeurIPS 2023 yielded a variety of results. Here's a summary of some notable papers in this area:

  1. Synthcity: A Benchmark Framework for Diverse Use Cases of Tabular Synthetic Data

    • Authors: Zhaozhi Qian, Rob Davis, Mihaela van der Schaar
    • Abstract: This paper introduces Synthcity, an open-source Python library for benchmarking synthetic data generators. It discusses differential privacy in the context of diverse data modalities and use cases, including privacy preservation.
    • Relevant Page: 11
    • Link to Paper

  2. Minimax Risks and Optimal Procedures for Estimation under Functional Local Differential Privacy

    • Authors: Bonwoo Lee, Jeongyoun Ahn, Cheolwoo Park
    • Relevant Pages: 1, 2
    • Link to Paper

  3. Counting Distinct Elements Under Person-Level Differential Privacy

    • Authors: Thomas Steinke, Alexander Knop
    • Abstract: This study addresses the challenge of counting the number of distinct elements in a dataset under the constraint of person-level differential privacy.
    • Relevant Page: 1
    • Link to Paper

  4. FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

    • Authors: Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han
    • Abstract: FedFed, a novel approach in federated learning to address data heterogeneity, discusses differential privacy as a framework for data privacy.
    • Relevant Page: 3
    • Link to Paper

  5. Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy

    • Authors: Chendi Wang, Buxin Su, Jiayuan Ye, Reza Shokri, Weijie Su
    • Relevant Page: 11
    • Link to Paper

  6. Dynamic Personalized Federated Learning with Adaptive Differential Privacy

    • Authors: Xiyuan Yang, Wenke Huang, Mang Ye
    • Abstract: This paper proposes a federated learning method with dynamic personalization and adaptive differential privacy.
    • Relevant Pages: 4, 5
    • Link to Paper

  7. Training Private Models That Know What They Don’t Know

    • Authors: Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot
    • Abstract: The paper investigates selective classifiers under differential privacy constraints and proposes a novel evaluation mechanism.
    • Relevant Pages: 11, 2, 3
    • Link to Paper

  8. Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency

    • Authors: Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Suggala
    • Relevant Pages: 14, 10, 11
    • Link to Paper

  9. Adaptive Privacy Composition for Accuracy-first Mechanisms

    • Authors: Ryan Rogers, Gennady Samorodnitsk, Steven Wu, Aaditya Ramdas
    • Relevant Page: 1
    • Link to Paper

  10. The Target-Charging Technique for Privacy Analysis across Interactive Computations

These papers cover a range of topics related to differential privacy, including synthetic data generation, federated learning, and privacy-preserving algorithms in machine learning.