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Poster: Vogue: Faster Computation of Private Heavy Hitters

Jangir, P and Koti, N and Kukkala, VB and Patra, A and Raj Gopal, B and Sangal, S (2022) Poster: Vogue: Faster Computation of Private Heavy Hitters. In: CCS '22: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 7 - 11 November 2022, Los Angeles, pp. 3371-3373.

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Official URL: https://doi.org/10.1145/3548606.3563544


Consider a set of N clients, each of which holds a private input string. An input string that is held by at least τ clients is defined as a τ-heavy hitter. In various application scenarios, data-aggregation servers are interested in learning τ-heavy hitters. To ensure that the servers do not learn anything about client input in the process, the problem of identifying heavy hitters privately is gaining popularity. Towards this, we design a novel system called Vogue, which provides improved efficiency as well as security guarantees over the state-of-the-art system of Poplar. Concretely, Vogue provides up to 27x efficiency improvement over Poplar when considering 400,000 clients who hold 256-bit input strings. Moreover, Vogue overcomes intermediate information leakages present in Poplar and guarantees full security in the presence of a malicious adversary. In the process of designing Vogue, we also design secure and efficient protocols for stable compaction and shuffle, each of which improves over its respective state-of-the-art.

Item Type: Conference Poster
Publication: Proceedings of the ACM Conference on Computer and Communications Security
Publisher: Association for Computing Machinery
Additional Information: The copyright for this article belongs to Association for Computing Machinery
Keywords: Cryptography; Efficiency, Application scenario; Data aggregation; Efficiency improvement; Fast computation; Heavy-hitter; Input string; Learn+; Secure multi-party computation; Shuffle; State-of-the-art system, Compaction
Department/Centre: Division of Physical & Mathematical Sciences > Mathematics
Date Deposited: 13 Jan 2023 10:16
Last Modified: 13 Jan 2023 10:16
URI: https://eprints.iisc.ac.in/id/eprint/79134

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