Privacy-Preserving Data Aggregation with Probabilistic Range Validation

Conference Paper (2021)
Author(s)

Florine W. Dekker (TU Delft - Cyber Security)

Z Erkin (TU Delft - Cyber Security)

Research Group
Cyber Security
Copyright
© 2021 Florine W. Dekker, Z. Erkin
DOI related publication
https://doi.org/10.1007/978-3-030-78375-4_4
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Florine W. Dekker, Z. Erkin
Research Group
Cyber Security
Volume number
12727
Pages (from-to)
79-98
ISBN (print)
978-3-030-78374-7
ISBN (electronic)
978-3-030-78375-4
Reuse Rights

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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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