Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Handbook of Applied Cryptography
Handbook of Applied Cryptography
New Constructions and Practical Applications for Private Stream Searching (Extended Abstract)
SP '06 Proceedings of the 2006 IEEE Symposium on Security and Privacy
Inverted files for text search engines
ACM Computing Surveys (CSUR)
CADS: continuous authentication on data streams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Proof-infused streams: enabling authentication of sliding window queries on streams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Predicate Privacy in Encryption Systems
TCC '09 Proceedings of the 6th Theory of Cryptography Conference on Theory of Cryptography
Secure outsourced aggregation via one-way chains
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Predicate encryption supporting disjunctions, polynomial equations, and inner products
EUROCRYPT'08 Proceedings of the theory and applications of cryptographic techniques 27th annual international conference on Advances in cryptology
Private searching on streaming data
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
Towards a secure data stream management system
TEAA'05 Proceedings of the 31st VLDB conference on Trends in Enterprise Application Architecture
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In a data streaming model, records or documents are pushed from a data owner, via untrusted third-party servers, to a large number of users with matching interests. The match in interest is calculated from the correlation between each pair of document and user query. For scalability and availability reasons, this calculation is delegated to the servers, which gives rise to the need to protect the privacy of the documents and user queries. In addition, the users need to guard against the eventuality of a server distorting the correlation score of the documents to manipulate which documents are highlighted to certain users. In this paper, we address the aforementioned privacy and verifiability challenges. We introduce the first cryptographic scheme which concurrently safeguards the privacy of the documents and user queries in such a data streaming model, while enabling users to verify the correlation scores obtained. We provide techniques to bound the computation demand in decrypting the correlation scores, and we demonstrate the overall practicality of the scheme through experiments with real data.