Sanyal, A; Kusner, MJ; Gascón, A; Kanade, V; (2018) TAPAS: Tricks to accelerate (encrypted) prediction as a service. In: Proceedings of the Thirty-fifth International Conference on Machine Learning. (pp. pp. 4490-4499). PMLR: Stockholm, Sweden.

Preview

Text sanyal18a.pdf - Published Version Download (613kB) | Preview

Abstract

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 amount of computation and communication required between client and server. Fully homomprphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.

Download activity - last month

Export as CSVJSONXML

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item