Cryptology ePrint Archive: Report 2018/206

Reading in the Dark: Classifying Encrypted Digits with Functional Encryption

Edouard Dufour-Sans and Romain Gay and David Pointcheval

Abstract: As machine learning grows into a ubiquitous technology that finds many interesting applications, the privacy of data is becoming a major concern. This paper deals with machine learning and encrypted data. Namely, our contribution is twofold: we first build a new Functional Encryption scheme for quadratic multi-variate polynomials, which outperforms previous schemes. It enables the efficient computation of quadratic polynomials on encrypted vectors, so that only the result is in clear. We then turn to quadratic networks, a class of machine learning models, and show that their design makes them particularly suited to our encryption scheme. This synergy yields a technique for efficiently recovering a plaintext classification of encrypted data. Eventually, we prototype our construction and run it on the MNIST dataset to demonstrate practical relevance. We obtain 97.54% accuracy, with decryption and encryption taking few seconds.

Category / Keywords: applications / Machine Learning on Encrypted Data, Functional Encryption, Quadratic polynomials

Date: received 21 Feb 2018, last revised 26 Oct 2020

Contact author: edufoursans at ens fr

Available format(s): PDF | BibTeX Citation

Version: 20201026:231424 (All versions of this report)

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