Differentially Private Confidence Intervals for Empirical Risk Minimization

Preprint English OPEN
Wang, Yue; Kifer, Daniel; Lee, Jaewoo; (2018)
  • Subject: Statistics - Machine Learning | Computer Science - Learning | Computer Science - Cryptography and Security
    arxiv: Computer Science::Cryptography and Security | Computer Science::Databases

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the... View more
  • References (47)
    47 references, page 1 of 5

    [1] Ipums-usa, 2017.

    [2] Minnesota population center. integrated public use microdata series, international: Version 6.5 brazil, 2017.

    [3] J. Acharya, Z. Sun, and H. Zhang. Di erentially private testing of identity and closeness of discrete distributions. arXiv preprint arXiv:1707.05128, 2017.

    [4] A. F. Barrientos, J. P. Reiter, A. Machanavajjhala, and Y. Chen. Di erentially private signi cance tests for regression coe cients. arXiv preprint arXiv:1705.09561, 2017.

    [5] R. Bassily, A. Smith, and A. Thakurta. Private empirical risk minimization: E cient algorithms and tight error bounds. In Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on, pages 464{473. IEEE, 2014.

    [6] A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: the sulq framework. In Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pages 128{138. ACM, 2005.

    [7] M. Bun and T. Steinke. Concentrated di erential privacy: Simpli cations, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635{658. Springer, 2016.

    [8] B. Cai, C. Daskalakis, and G. Kamath. Privit: Private and sample e cient identity testing. In International Conference on Machine Learning, pages 635{644, 2017.

    [9] O. Chapelle. Training a support vector machine in the primal. Neural computation, 19(5):1155{1178, 2007.

    [10] K. Chaudhuri, C. Monteleoni, and A. D. Sarwate. Di erentially private empirical risk minimization. Journal of Machine Learning Research, 12(Mar):1069{1109, 2011.

  • Metrics
    No metrics available
Share - Bookmark