Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
The peer sampling service: experimental evaluation of unstructured gossip-based implementations
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
On the false-positive rate of Bloom filters
Information Processing Letters
Distributed Private Data Analysis: Simultaneously Solving How and What
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Efficient network aware search in collaborative tagging sites
Proceedings of the VLDB Endowment
Privacy-preserving indexing of documents on the network
The VLDB Journal — The International Journal on Very Large Data Bases
Computational Differential Privacy
CRYPTO '09 Proceedings of the 29th Annual International Cryptology Conference on Advances in Cryptology
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
The GOSSPLE anonymous social network
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
On the relation between differential privacy and quantitative information flow
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Public-key encrypted bloom filters with applications to supply chain integrity
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
How much is enough? choosing ε for differential privacy
ISC'11 Proceedings of the 14th international conference on Information security
Performance-oriented privacy-preserving data integration
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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In this paper, we consider the scenario in which the profile of a user is represented in a compact way, as a Bloom filter, and the main objective is to privately compute in a distributed manner the similarity between users by relying only on the Bloom filter representation. In particular, we aim at providing a high level of privacy with respect to the profile even if a potentially unbounded number of similarity computations take place, thus calling for a non-interactive mechanism. To achieve this, we propose a novel non-interactive differentially private mechanism called BLIP (for BLoom-and-flIP) for randomizing Bloom filters. This approach relies on a bit flipping mechanism and offers high privacy guarantees while maintaining a small communication cost. Another advantage of this non-interactive mechanism is that similarity computation can take place even when the user is offline, which is impossible to achieve with interactive mechanisms. Another of our contributions is the definition of a probabilistic inference attack, called the "Profile Reconstruction attack", that can be used to reconstruct the profile of an individual from his Bloom filter representation. More specifically, we provide an analysis of the protection offered by BLIP against this profile reconstruction attack by deriving an upper and lower bound for the required value of the differential privacy parameter ε.