Corpus ID: 203734625

PINFER: Privacy-Preserving Inference for Machine Learning

@article{Joye2019PINFERPI,
  title={PINFER: Privacy-Preserving Inference for Machine Learning},
  author={M. Joye and F. Petitcolas},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01865}
}
  • M. Joye, F. Petitcolas
  • Published 2019
  • Computer Science
  • ArXiv
  • The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres appear limited to enterprise customers due to their complexity, while general multi-party computation techniques require a large number of message exchanges. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 31 REFERENCES
    PINFER: Privacy-Preserving Inference
    2
    SecureML: A System for Scalable Privacy-Preserving Machine Learning
    454
    A privacy-preserving protocol for neural-network-based computation
    88
    Privacy Preserving Data Mining
    1817
    Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation
    60
    Secure Dot Product of Outsourced Encrypted Vectors and its Application to SVM
    10
    Machine Learning Classification over Encrypted Data
    404
    Privacy-preserving Prediction
    33
    Improving the DGK comparison protocol
    41