Cryptology ePrint Archive: Report 2020/522

Aggregated Private Information Retrieval: A First Practical Implementation to Support Large-Scale Disease Analytics

Lukas Helminger and Daniel Kales and Christian Rechberger and Roman Walch

Abstract: With the outbreak of the coronavirus, governments rely more and more on location data shared by European mobile network operators to monitor the advancements of the disease. In order to comply with often strict privacy requirements, this location data, however, has to be anonymized, limiting its usefulness for making statements about a filtered part of the population, like already infected people. In this research, we aim to assist with the disease tracking efforts by designing a protocol to detect coronavirus hotspots from mobile data while still maintaining compliance with privacy expectations. We use various state-of-the-art privacy-preserving cryptographic primitives to design a protocol that can best be described as aggregated private information retrieval (APIR). Our protocol is based on homomorphic encryption, with additional measures to protect against malicious requests from clients. We have implemented our APIR protocol in the SEAL library and tested it for parameters suitable to create a coronavirus hotspot map for entire nationstates. This demonstrates that it is feasible to apply our APIR protocol to support nationwide disease analysis while still preserve the privacy of infected people.

Category / Keywords: cryptographic protocols / FHE, APIR, Sars-CoV-2, Corona virus, BFV

Date: received 5 May 2020

Contact author: lukas helminger at iaik tugraz at,daniel kales@iaik tugraz at,christian rechberger@tugraz at,roman walch@iaik tugraz at

Available format(s): PDF | BibTeX Citation

Version: 20200505:151840 (All versions of this report)

Short URL: ia.cr/2020/522


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