Privacy protection for RFID data
Proceedings of the 2009 ACM symposium on Applied Computing
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing healthcare data: a case study on the blood transfusion service
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing location-based RFID data
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering
Walking in the crowd: anonymizing trajectory data for pattern analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Transactions on Data Privacy
Satisfying privacy requirements: one step before anonymization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
Anonymizing classification data using rough set theory
Knowledge-Based Systems
A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard
Computer Methods and Programs in Biomedicine
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We consider the problem of publishing sensitive transaction data with privacy preservation. High dimensionality of transaction data poses unique challenges on data privacy and data utility. On one hand, re-identification attacks tend to use a subset of items that infrequently occur in transactions, called moles. On the other hand, data mining applications typically depend on subsets of items that frequently occur in transactions, called nuggets. Thus the problem is how to eliminate all moles while retaining nuggets as much as possible. A challenge is that moles and nuggets are multi-dimensional with exponential growth and are tangled together by shared items. We present a novel and scalable solution to this problem. The novelty lies in a compact border data structure that eliminates the need of generating all moles and nuggets.