Privacy-Preserving Data Aggregation with Probabilistic Range Validation

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 such 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 an asynchronous probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Our protocol is robust to user dropouts and is non-interactive apart from the registration phase. We describe several optional extensions to our protocol for temporal aggregation, dynamic user joins and leaves, and differential privacy. We analyse our protocol in terms of security, privacy, and detection rate. Finally, we compare the runtime complexity of our protocol with a selection of related protocols.