A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Disclosure Limitation of Sensitive Rules
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An integer programming approach for frequent itemset hiding
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Privacy risks in health databases from aggregate disclosure
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Hiding sensitive knowledge without side effects
Knowledge and Information Systems
Association Rule Hiding for Data Mining
Association Rule Hiding for Data Mining
Hiding Sequential and Spatiotemporal Patterns
IEEE Transactions on Knowledge and Data Engineering
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Anonymous Publication of Sensitive Transactional Data
IEEE Transactions on Knowledge and Data Engineering
Hiding classification rules for data sharing with privacy preservation
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Sequence datasets are encountered in a plethora of applications spanning from web usage analysis to healthcare studies and ubiquitous computing. Disseminating such datasets offers remarkable opportunities for discovering interesting knowledge patterns, but may lead to serious privacy violations if sensitive patterns, such as business secrets, are disclosed. In this work, we consider how to sanitize data to prevent the disclosure of sensitive patterns during sequential pattern mining, while ensuring that the nonsensitive patterns can still be discovered. First, we re-define the problem of sequential pattern hiding to capture the information loss incurred by sanitization in terms of both events' modification (distortion) and lost nonsensitive knowledge patterns (side-effects). Second, we model sequences as graphs and propose two algorithms to solve the problem by operating on the graphs. The first algorithm attempts to sanitize data with minimal distortion, whereas the second focuses on reducing the side-effects. Extensive experiments show that our algorithms outperform the existing solution in terms of data distortion and side-effects and are more efficient.