State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Preserving Privacy by De-Identifying Face Images
IEEE Transactions on Knowledge and Data Engineering
Hiding Sensitive Association Rules with Limited Side Effects
IEEE Transactions on Knowledge and Data Engineering
Privacy preserving itemset mining through fake transactions
Proceedings of the 2007 ACM symposium on Applied computing
MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining
Advanced Engineering Informatics
Efficient algorithms for distortion and blocking techniques in association rule hiding
Distributed and Parallel Databases
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
Privacy Preserving Data Mining Research: Current Status and Key Issues
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Optimization for MASK Scheme in Privacy Preserving Data Mining for Association Rules
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Maintenance of sanitizing informative association rules
Expert Systems with Applications: An International Journal
FRAPP: a framework for high-accuracy privacy-preserving mining
Data Mining and Knowledge Discovery
Privacy preserving itemset mining through noisy items
Expert Systems with Applications: An International Journal
Employing PRBAC for privacy preserving data publishing
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
HHUIF and MSICF: Novel algorithms for privacy preserving utility mining
Expert Systems with Applications: An International Journal
Privacy-preserving discovery of frequent patterns in time series
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
Suppressing microdata to prevent probabilistic classification based inference
SDM'05 Proceedings of the Second VDLB international conference on Secure Data Management
Knowledge hiding from tree and graph databases
Data & Knowledge Engineering
Ensuring data security against knowledge discovery in distributed information systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
An effective approach for hiding sensitive knowledge in data publishing
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Privacy-preserving frequent itemsets mining via secure collaborative framework
Security and Communication Networks
Privacy Aware Data Management and Chase
Fundamenta Informaticae - Special issue ISMIS'05
Specialization in i* strategic rationale diagrams
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Collusion-Free Privacy Preserving Data Mining
International Journal of Intelligent Information Technologies
Secure Two-Party Association Rule Mining Based on One-Pass FP-Tree
International Journal of Information Security and Privacy
Effective sanitization approaches to hide sensitive utility and frequent itemsets
Intelligent Data Analysis
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The current trend in the application space towards systems of loosely coupled and dynamically bound components that enables just-in-time integration jeopardizes the security of information that is shared between the broker, the requester, and the provider at runtime. In particular, new advances in data mining and knowledge discovery, that allow for the extraction of hidden knowledge in enormous amount of data, impose new threats on the seamless integration of information. In this paper, we consider the problem of building privacy preserving algorithms for one category of data mining techniques, the association rule mining. We introduce new metrics in order to demonstrate how security issues can be taken into consideration in the general framework of association rule mining, and we show that the complexity of the new heuristics is similar to this of the original algorithms.