PINFER: Privacy-Preserving Inference

Logistic Regression, Support Vector Machines, and More, over Encrypted Data
  • Marc JoyeEmail author
  • Fabien Petitcolas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11737)


The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. This paper proposes a variety of protocols for privacy-preserving regression and classification that (i) only require additively homomorphic encryption algorithms, (ii) limit interactions to a mere request and response, and (iii) that can be used directly for important machine-learning algorithms such as logistic regression and SVM classification. The basic protocols are then extended and applied to simple feed-forward neural networks.


Machine learning as a service Linear regression Logistic regression Support vector machines Feed-forward neural networks Data privacy Additively homomorphic encryption 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.OneSpanBrusselsBelgium

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