Discovering patterns in sequences of events
Artificial Intelligence
Some average measures in m-ary search trees
Information Processing Letters
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving mining of association rules
Information Systems - Knowledge discovery and data mining (KDD 2002)
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy-preserving collaborative association rule mining
Journal of Network and Computer Applications
A General Model for Sequential Pattern Mining with a Progressive Database
IEEE Transactions on Knowledge and Data Engineering
Hiding co-occurring frequent itemsets
Proceedings of the 2009 EDBT/ICDT Workshops
Hiding co-occurring sensitive patterns in progressive databases
Proceedings of the 2010 EDBT/ICDT Workshops
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Recently, privacy preservation in data mining is an important area of research. It can be done in several ways. Hiding of sensitive patterns is one such important method. In a typical scenario, multiple parties may wish to collaborate to extract interesting global patterns from their integrated data items without revealing their respective local data to each other. Typical applications include finance, medical research, retail sales etc. In certain cases, there may be some patterns whose co-occurrence may lead to revelation of sensitive information. In the present work, hiding of co-occurring sensitive patterns dynamically from distributed progressive databases has been proposed. In addition in the proposed work dynamic priorities have also been coupled, along with the items. This helps to decide which patterns to hide from the set of sensitive patterns. The various partitioning scenarios for distributed databases that have been used include horizontal, vertical and arbitrary. In all such cases, the data is distributive progressive in nature i.e., old data items may become obsolete whereas new data items may be treated as more significant.