Publications

Author	Chaudhari, Harsh; Choudhury, Ashish; Patra, Arpita; Suresh, Ajith
Date	2019
Type	Conference Proceedings
Abstract	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 2ℓ 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. In the malicious setting, our protocol requires communication of 4 elements 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 a 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 malicious world, invariably perform better than the existing constructions.
Conference	10th ACM Cloud Computing Security Workshop (CCSW'19)
Isbn	978-1-4503-6826-1
In	CCSW'19: Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, p.81-92
Publisher	ACM
Url	https://tubiblio.ulb.tu-darmstadt.de/id/eprint/160562