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SLAP: Simpler, Improved Private Stream Aggregation from Ring Learning with Errors

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Abstract

Private Stream Aggregation (PSA) protocols perform secure aggregation of time-series data without leaking information about users’ inputs to the aggregator. Previous work in post-quantum PSA used the Ring Learning with Errors (RLWE) problem indirectly via homomorphic encryption (HE), leading to a needlessly complex and intensive construction. In this work, we present SLAP, the first PSA protocol that is directly constructed from the RLWE problem to gain post-quantum security. By nature of our white-box approach, SLAP is simpler and more efficient than previous PSA that uses RLWE indirectly through the black box of HE. We also show how to apply state-of-the-art optimizations for lattice-based cryptography to greatly improve the practical performance of SLAP. The communication overhead of SLAP is much less than in previous work, with decreases of up to 99.96% in ciphertext sizes as compared to previous work in RLWE-based PSA. We demonstrate a speedup of 20.76x over the previous state-of-the-art RLWE-based PSA work’s aggregation and show that SLAP achieves a throughput of 390,691 aggregations per second for 1000 users. We also compare SLAP to other state-of-the-art post-quantum PSA and show that SLAP is comparable in latency and shows improvement in throughput when compared to these works, and we compare the qualitative features of these schemes with regards to practical usability.

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Data Availability

The datasets generated and analyzed in this work are available from the first author upon reasonable request.

Notes

  1. While an element \({\textbf{a}}_i\) could be zero, this happens with only negligible probability.

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Funding

This work was supported by Facebook as a winner of the Role of Applied Cryptography in a Privacy-Focused Advertising Ecosystem Facebook RFP. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

    Jonathan Takeshita & Taeho Jung

  2. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Ryan Karl

  3. Department of Mathematics, University of Washington, Seattle, WA, USA

    Ting Gong

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  1. Jonathan Takeshita
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Correspondence to Taeho Jung.

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Takeshita, J., Karl, R., Gong, T. et al. SLAP: Simpler, Improved Private Stream Aggregation from Ring Learning with Errors. J Cryptol 36, 8 (2023). https://doi.org/10.1007/s00145-023-09450-w

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