Paper 2017/1234

High-Precision Privacy-Preserving Real-Valued Function Evaluation

Christina Boura, Ilaria Chillotti, Nicolas Gama, Dimitar Jetchev, Stanislav Peceny, and Alexander Petric


We propose a novel multi-party computation protocol for evaluating continuous real-valued functions with high numerical precision. Our method is based on approximations with Fourier series and uses at most two rounds of communication during the online phase. For the offline phase, we propose a trusted-dealer and honest-but-curious aided solution, respectively. We apply our algorithm to train a logistic regression classifier via a variant of Newton's method (known as IRLS) to compute unbalanced classification problems that detect rare events and cannot be solved using previously proposed privacy-preserving optimization algorithms (e.g., based on piecewise-linear approximations of the sigmoid function). Our protocol is efficient as it can be implemented using standard quadruple-precision floating point arithmetic. We report multiple experiments and provide a demo application that implements our algorithm for training a logistic regression model.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. MINOR revision.Financial Crypto 2017
Multiparty ComputationNumerical PrecisionFourierMachine Learning
Contact author(s)
nicolas @ inpher io
2017-12-22: received
Short URL
Creative Commons Attribution


      author = {Christina Boura and Ilaria Chillotti and Nicolas Gama and Dimitar Jetchev and Stanislav Peceny and Alexander Petric},
      title = {High-Precision Privacy-Preserving Real-Valued Function Evaluation},
      howpublished = {Cryptology ePrint Archive, Paper 2017/1234},
      year = {2017},
      note = {\url{}},
      url = {}
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