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Solving Set Relations with Secure Bloom Filters Keeping Cardinality Private
... Therefore, we concentrate on providing a privacy-preserving solution for the 2nd effort currently done within Germany. Our proposed solution makes use of our previous works [3], [4] which allows a non-trusted third party to privately compute operations and relations on sets using Bloom filters data structure. Such data structure allows one to represent a large set of elements in a simple tabular of bits which could provides obfuscation and privacy on the set. ...
... In [10], we applied our Bloom filter based construction to several use cases of post-mortem mobile device tracking. In our former work [4], we have shown that this alternative approach based on Bloom filter could be used to secure data while preserving the ability of performing relevant tests or computations on the private data. Bloom filters have been used in many different scenarios as presented in [11]. ...
... As recently proposed in [4], the Bloom filter data construction could allow to privately represent sets of elements and at the same time enable performance-saving computation on them. Exactly due to this performance-saving privacy extension, we argue that our approach also suits for such massive data sets like mobile access logfiles. ...
While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.
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