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Privacy-Preserving Data Aggregation with Probabilistic Range Validation

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Applied Cryptography and Network Security (ACNS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12727))

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Abstract

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely.

We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.

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Authors and Affiliations

  1. Cyber Security Group, Delft University of Technology, Delft, The Netherlands

    F. W. Dekker & Zekeriya Erkin

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  1. F. W. Dekker
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Correspondence to F. W. Dekker .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Kazue Sako

  2. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Nils Ole Tippenhauer

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Dekker, F.W., Erkin, Z. (2021). Privacy-Preserving Data Aggregation with Probabilistic Range Validation. In: Sako, K., Tippenhauer, N.O. (eds) Applied Cryptography and Network Security. ACNS 2021. Lecture Notes in Computer Science(), vol 12727. Springer, Cham. https://doi.org/10.1007/978-3-030-78375-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-78375-4_4

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