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TAPAS : tricks to accelerate (encrypted) prediction as a service
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Sanyal, Amartya, Kusner, Matt, Gascon, Adria and Kanade, Varun (2018) TAPAS : tricks to accelerate (encrypted) prediction as a service. In: 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 10-15 Jul 2018. Published in: Proceedings of the 35th International Conference on Machine Learning, 80 pp. 4490-4499. ISSN 2640-3498.
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WRAP-TAPAS-tricks-accelerate-(encrypted)-prediction-service-Gascon-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1060Kb) | Preview |
Official URL: http://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.
| Item Type: | Conference Item (Paper) | |||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Machine learning, Cryptography | |||||||||
| Journal or Publication Title: | Proceedings of the 35th International Conference on Machine Learning | |||||||||
| ISSN: | 2640-3498 | |||||||||
| Editor: | Dy, Jennifer and Krause, Andreas | |||||||||
| Official Date: | 2018 | |||||||||
| Dates: |
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| Volume: | 80 | |||||||||
| Page Range: | pp. 4490-4499 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 28 May 2019 | |||||||||
| Date of first compliant Open Access: | 28 May 2019 | |||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||
| Title of Event: | 35th International Conference on Machine Learning, ICML 2018 | |||||||||
| Type of Event: | Conference | |||||||||
| Location of Event: | Stockholm, Sweden | |||||||||
| Date(s) of Event: | 10-15 Jul 2018 | |||||||||
| Open Access Version: |
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