Privacy-Preserving Data Aggregation with Probabilistic Range Validation

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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.
Original languageEnglish
Title of host publicationInternational Conference on Applied Cryptography and Network Security
EditorsKazue Sako, Nils Ole Tippenhauer
Place of PublicationKamakura, Japan
PublisherSpringer Nature
Number of pages20
ISBN (Electronic)978-3-030-78375-4
ISBN (Print)978-3-030-78374-7
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Privacy
  • Data aggregation
  • Applied cryptography
  • Hypergraphs


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