Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving distributed k-means clustering over arbitrarily partitioned data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Privacy Preserving ID3 Algorithm over Horizontally Partitioned Data
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
A privacy-preserving protocol for neural-network-based computation
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Privacy preserving ID3 using Gini Index over horizontally partitioned data
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy-Preserving decision trees over vertically partitioned data
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Privacy-Preserving collaborative association rule mining
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
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Association rule mining provides useful knowledge from raw data in different applications such as health, insurance, marketing and business systems. However, many real world applications are distributed among two or more parties, each of which wants to keep its sensitive information private, while they collaboratively gaining some knowledge from their data. Therefore, secure and distributed solutions are needed that do not have a central or third party accessing the parties' original data. In this paper, we present a new protocol for privacy-preserving association rule mining to overcome the security flaws in existing solutions, with better performance, when data is vertically partitioned among two or more parties. Two sub-protocols for secure binary dot product and cardinality of set intersection for binary vectors are also designed which are used in the main protocols as building blocks.