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ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction

Chaudhari, Harsh and Choudhury, Ashish and Patra, Arpita and Suresh, Ajith (2019) ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction. In: ACM SIGSAC Conference on Cloud Computing Security Workshop (CCSW), November 2019, pp. 81-92.

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Official URL: https://doi.org/10.1145/3338466.3358922


The concrete efficiency of secure computation has been the focus of many recent works. In this work, we present concretely efficient protocols for secure 3-party computation (3PC) over a ring of integers modulo 2^l tolerating one corruption, both with semi-honest and malicious security. Owing to the fact that computation over ring emulates computation over the real-world system architectures, secure computation over ring has gained momentum of late. Cast in the offline-online paradigm, our constructions present the most efficient online phase in concrete terms. In the semi-honest setting, our protocol requires communication of 2 ring elements per multiplication gate during the online phase, attaining a per-party cost of less than one element. This is achieved for the first time in the regime of 3PC. In the malicious setting, our protocol requires communication of 4elements per multiplication gate during the online phase, beating the state-of-the-art protocol by 5 elements. Realized with both the security notions of selective abort and fairness, the malicious protocol with fairness involves slightly more communication than its counterpart with abort security for the output gates alone. We apply our techniques from 3PC in the regime of secure server-aided machine-learning (ML) inference for a range of prediction functions-- linear regression, linear SVM regression, logistic regression, and linear SVM classification. Our setting considers a model-owner with trained model parameters and a client with a query, with the latter willing to learn the prediction of her query based on the model parameters of the former. The inputs and computation are outsourced to a set of three non-colluding servers. Our constructions catering to both semi-honest and the malicious world, invariably perform better than the existing constructions.

Item Type: Conference Proceedings
Publisher: Association for Computing Machinery (ACM)
Additional Information: The copyright to this article belongs to the authors.
Keywords: cryptographic protocols / Secure Computation, Machine Learning, 3PC, Secure Prediction, PPML
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 05 Jul 2021 10:07
Last Modified: 05 Jul 2021 10:07
URI: http://eprints.iisc.ac.in/id/eprint/68757

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