TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Amartya Sanyal, Matt Kusner, Adria Gascon, Varun Kanade
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4490-4499, 2018.

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 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-sanyal18a, title = {{TAPAS}: Tricks to Accelerate (encrypted) Prediction As a Service}, author = {Sanyal, Amartya and Kusner, Matt and Gascon, Adria and Kanade, Varun}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {4490--4499}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/sanyal18a/sanyal18a.pdf}, url = {https://proceedings.mlr.press/v80/sanyal18a.html}, 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 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.} }
Endnote
%0 Conference Paper %T TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service %A Amartya Sanyal %A Matt Kusner %A Adria Gascon %A Varun Kanade %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-sanyal18a %I PMLR %P 4490--4499 %U https://proceedings.mlr.press/v80/sanyal18a.html %V 80 %X 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.
APA
Sanyal, A., Kusner, M., Gascon, A. & Kanade, V.. (2018). TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:4490-4499 Available from https://proceedings.mlr.press/v80/sanyal18a.html.

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