Privacy-Preserving Logistic Regression

@inproceedings{Samet2015PrivacyPreservingLR,
  title={Privacy-Preserving Logistic Regression},
  author={S. Samet},
  year={2015}
}
  • S. Samet
  • Published 2015
  • Logistic regression is an important statistical analysis methods widely used in research fields, including health, business and government. On the other hand preserving data privacy is a crucial aspect in every information system. Many privacy-preserving protocols have been proposed for different statistical techniques, with various data distributions, owners and users. In this paper, we propose a new method to securely compute logistic regression of data, privately shared among two or more… CONTINUE READING
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    References

    SHOWING 1-10 OF 16 REFERENCES
    Privacy Preserving Data Mining
    • 801
    • PDF
    Privacy-Preserving Linear Fisher Discriminant Analysis
    • 32
    Privacy-Preserving Bayesian Network for Horizontally Partitioned Data
    • S. Samet, A. Miri
    • Computer Science
    • 2009 International Conference on Computational Science and Engineering
    • 2009
    • 18
    On Private Scalar Product Computation for Privacy-Preserving Data Mining
    • 397
    • PDF
    Tools for privacy preserving distributed data mining
    • 955
    • PDF
    Privacy Preserving Data Mining Models And Algorithms
    • 97
    A method for obtaining digital signatures and public-key cryptosystems
    • 10,082
    • Highly Influential
    • PDF
    Security and Composition of Multiparty Cryptographic Protocols
    • R. Canetti
    • Computer Science, Mathematics
    • Journal of Cryptology
    • 2000
    • 1,151
    FairplayMP: a system for secure multi-party computation
    • 504
    • PDF
    Protocols for secure computations
    • A. Yao
    • Computer Science
    • FOCS 1982
    • 1982
    • 3,164