Random oracles are practical: a paradigm for designing efficient protocols
CCS '93 Proceedings of the 1st ACM conference on Computer and communications security
The Decision Diffie-Hellman Problem
ANTS-III Proceedings of the Third International Symposium on Algorithmic Number Theory
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
| Hi-index | 0.00 |
Privacy-preserving data mining aims at discovering beneficial information from a large amount of data without violating the privacy policy. Privacy-preserving association rules mining research has already generated many interesting results. Based on commutative encryptions and the Secure Multi-party Computation (SMC) theory, Kantarcioglu and Clifton [1] propose two protocols to implement privacy-preserving mining of association rules over horizontally partitioned data. The paper addresses its incorrect security proof and introduces a more well-founded proof. This paper also identifies several other errors in [1]. This kind of protocols and their proof are a concrete application of Secure Multi-party Computation, which is be of great significances to the privacy-preserving data mining studies based on SMC. Thus establishment of the correct proof methodology is important. This paper demonstrates the correct proof methodology by correcting the fault proof in [1]