Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Handbook of Applied Cryptography
Handbook of Applied Cryptography
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
The Piazza peer data management project
ACM SIGMOD Record
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Efficient query reformulation in peer data management systems
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Data sharing in the Hyperion peer database system
VLDB '05 Proceedings of the 31st international conference on Very large data bases
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
New foundations for efficient authentication, commutative cryptography, and private disjointness testing
Queries and updates in the coDB peer to peer database system
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Private queries in location based services: anonymizers are not necessary
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Simulatable Adaptive Oblivious Transfer
EUROCRYPT '07 Proceedings of the 26th annual international conference on Advances in Cryptology
Spatial Outsourcing for Location-based Services
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Schema mapping and query translation in heterogeneous P2P XML databases
The VLDB Journal — The International Journal on Very Large Data Bases
Preserving privacy and fairness in peer-to-peer data integration
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Nearest neighbor search with strong location privacy
Proceedings of the VLDB Endowment
An improved algorithm for computing logarithms over and its cryptographic significance (Corresp.)
IEEE Transactions on Information Theory
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Peer Data Management Systems (PDMS) are an attractive solution for managing distributed heterogeneous information. When a peer (client) requests data from another peer (server) with a different schema, translations of the query and its answer are done by a sequence of intermediate peers (translators). There are two privacy issues in this P2P data integration process: (i) answer privacy: no unauthorized parties (including the translators) should learn the query result; (ii) mapping privacy: the schema and the value mappings used by the translators to perform the translation should not be revealed to other peers. Elmeleegy and Ouzzani proposed the PPP protocol that is the first to support privacy-preserving querying in PDMS. However, PPP suffers from several shortcomings. First, PPP does not satisfy the requirement of answer privacy, because it is based on commutative encryption; we show that this issue can be fixed by adopting another cryptographic technique called oblivious transfer. Second, PPP adopts a weaker notion for mapping privacy, which allows the client peer to observe certain mappings done by translators. In this paper, we develop a lightweight protocol, which satisfies mapping privacy and extend it to a more complex one that facilitates parallel translation by peers. Furthermore, we consider a stronger adversary model where there may be collusions among peers and propose an efficient protocol that guards against collusions. We conduct an experimental study on the performance of the proposed protocols using both real and synthetic data. The results show that the proposed protocols not only achieve a better privacy guarantee than PPP, but they are also more efficient.