Designing a Bloom filter for differential file access
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Embedding expert knowledge and hypothetical data bases into a data base system
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
An architecture for fault tolerance in database systems
ACM '80 Proceedings of the ACM 1980 annual conference
Payload attribution via hierarchical bloom filters
Proceedings of the 11th ACM conference on Computer and communications security
Privacy-preserving payload-based correlation for accurate malicious traffic detection
Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
On the false-positive rate of Bloom filters
Information Processing Letters
Applications of Bloom Filters in Peer-to-peer Systems: Issues and Questions
NAS '08 Proceedings of the 2008 International Conference on Networking, Architecture, and Storage
Sharing Private Information Across Distributed Databases
NCA '09 Proceedings of the 2009 Eighth IEEE International Symposium on Network Computing and Applications
Privacy-preserving indexing of documents on the network
The VLDB Journal — The International Journal on Very Large Data Bases
Secure anonymous database search
Proceedings of the 2009 ACM workshop on Cloud computing security
Cryptographically Secure Bloom-Filters
Transactions on Data Privacy
Public key encryption that allows PIR queries
CRYPTO'07 Proceedings of the 27th 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
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Classification of DNA sequences using Bloom filters
Bioinformatics
A constraint satisfaction cryptanalysis of bloom filters in private record linkage
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
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Bloom filters are probabilistic data structures which permit to conveniently represent set membership. Their performance/memory efficiency makes them appealing in a huge variety of scenarios. Their probabilistic operation, along with the implicit data representation, yields some ambiguity on the actual data stored, which, in scenarios where cryptographic protection is unviable or unpractical, may be somewhat considered as a better than nothing privacy asset. Oddly enough, even if frequently mentioned, to the best of our knowledge the (soft) privacy properties of Bloom filters have never been explicitly quantified. This work aims to fill this gap. Starting from the adaptation of probabilistic anonymity metrics to the Bloom filter setting, we derive exact and (tightly) approximate formulae which permit to readily relate privacy properties with filter (and universe set) parameters. Using such relations, we quantitatively investigate the emerging privacy/utility trade-offs. We finally preliminary assess the advantages that a tailored insertion of a few extra (covert) bits achieves over the commonly employed strategy of increasing ambiguity via addition of random bits.