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TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
Amartya Sanyal · Matt Kusner · Adria Gascon · Varun Kanade

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #46

Machine learning methods are widely used for a variety of prediction problems. Prediction as a service is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the nature of computation and amount of communication required between client and server. Fully homomorphic encryption offers a way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The one drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine several ideas from the machine learning literature, particularly work on quantization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.

Author Information

Amartya Sanyal (University of Oxford)
Matt Kusner (Alan Turing Institute)
Adria Gascon (The Alan Turing Institute / Warwick University)
Varun Kanade (University of Oxford)

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