A data distortion by probability distribution
ACM Transactions on Database Systems (TODS)
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
A k-Anonymity Clustering Method for Effective Data Privacy Preservation
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Privately detecting bursts in streaming, distributed time series data
Data & Knowledge Engineering
A tree-based approach to preserve the privacy of software engineering data and predictive models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
A privacy protection technique for publishing data mining models and research data
ACM Transactions on Management Information Systems (TMIS)
Target-based privacy preserving association rule mining
Proceedings of the 2011 ACM Symposium on Applied Computing
An improved EDP algorithm to privacy protection in data mining
BI'11 Proceedings of the 2011 international conference on Brain informatics
Protecting Privacy Against Record Linkage Disclosure: A Bounded Swapping Approach for Numeric Data
Information Systems Research
Breaching Euclidean distance-preserving data perturbation using few known inputs
Data & Knowledge Engineering
Class-Restricted Clustering and Microperturbation for Data Privacy
Management Science
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Due to growing concerns about the privacy of personal information, organizations that use their customers' records in data mining activities are forced to take actions to protect the privacy of the individuals. A frequently used disclosure protection method is data perturbation. When used for data mining, it is desirable that perturbation preserves statistical relationships between attributes, while providing adequate protection for individual confidential data. To achieve this goal, we propose a kd-tree based perturbation method, which recursively partitions a data set into smaller subsets such that data records within each subset are more homogeneous after each partition. The confidential data in each final subset are then perturbed using the subset average. An experimental study is conducted to show the effectiveness of the proposed method.