A randomized protocol for signing contracts
Communications of the ACM
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
All-or-nothing disclosure of secrets
Proceedings on Advances in cryptology---CRYPTO '86
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Oblivious transfer and polynomial evaluation
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New Privacy Model for Web Surfing
NGITS '02 Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
An architecture for privacy-preserving mining of client information
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Composition of Secure Multi-Party Protocols: A Comprehensive Study
Composition of Secure Multi-Party Protocols: A Comprehensive Study
PRAW—A PRivAcy model for the Web: Research Articles
Journal of the American Society for Information Science and Technology
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
ACM SIGKDD Explorations Newsletter
Depth first generation of frequent patterns without candidate generation
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Top-down and bottom-up strategies for incremental maintenance of frequent patterns
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Mining disjunctive consequent association rules
Applied Soft Computing
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Effective product assignment based on association rule mining in retail
Expert Systems with Applications: An International Journal
Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
Knowledge-Based Systems
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
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Privacy concerns have become an important issue in Data Mining. This paper deals with the problem of association rule mining from distributed vertically partitioned data with the goal of preserving the confidentiality of each database. Each site holds some attributes of each transaction, and the sites wish to work together to find globally valid association rules without revealing individual transaction data. This problem occurs, for example, when the same users access several electronic shops purchasing different items in each. We present two algorithms for discovering frequent itemsets and for calculating the confidence of the rules. We then analyze the algorithms privacy properties, and compare them to other published algorithms.