Blocking-aware private record linkage
Proceedings of the 2nd international workshop on Information quality in information systems
Distributed spatio-temporal similarity search
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
k nearest neighbor classification across multiple private databases
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Mining multiple private databases using a kNN classifier
Proceedings of the 2007 ACM symposium on Applied computing
Top-k Monitoring in Wireless Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Preserving data privacy in outsourcing data aggregation services
ACM Transactions on Internet Technology (TOIT) - Special Issue on the Internet and Outsourcing
Finding the K highest-ranked answers in a distributed network
Computer Networks: The International Journal of Computer and Telecommunications Networking
Privacy-preserving distributed network troubleshooting—bridging the gap between theory and practice
ACM Transactions on Information and System Security (TISSEC)
Privacy preserving indexing for eHealth information networks
Proceedings of the 20th ACM international conference on Information and knowledge management
Private over-threshold aggregation protocols
ICISC'12 Proceedings of the 15th international conference on Information Security and Cryptology
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Advances in distributed service-oriented computing and global communications have formed a strong technology push for large scale data integration among organizations and enterprises. However, concerns about data privacy become increasingly important for large scale mission-critical data integration applications. Ideally, given a database query spanning multiple private databases, we wish to compute the answer to the query without revealing any additional information of each individual database apart from the query result. In practice, we may relax this constraint to allow efficient information integration while minimizing the information disclosure. In this paper, we propose an efficient decentralized peer-to-peer protocol for supporting aggregate queries over multiple private databases while respecting the privacy constraints of participants. The paper has three main contributions. First, it formalizes the notion of loss of privacy in terms of information revealed at individual participating databases. Second, it presents a novel probabilistic decentralized protocol for top k selection across multiple private databases that minimizes the loss of privacy. Third, it experimentally evaluates the protocol in terms of its correctness, efficiency and privacy characteristics.