Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Publishing Sensitive Transactions for Itemset Utility
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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
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In this paper, we study the problem of anonymizing high dimensional location-based RFID data for mining or research purposes. We consider the case where RFID cards are used for purchasing in place of magnetic cards. Databases containing such transactions of card holders could be very huge in number of records (equals to number of users) and dimensions (could be equal to the domain of locations where users are allowed to use their cards). This huge database containing user's purchasing history can be mined to find interesting knowledge. At the same time publication of data would cause re-identification attacks by adversaries who have partial knowledge about transactions. Therefore, before publishing transactional data, it should be made k-anonymous. However, traditional k-anonymity methods were designed to k-anonymize low dimensional databases and are not scalable much to produce good results when it comes to k-anonymous large high dimensional databases. In this paper, we provide a solution modeling k-anonymity principle to protect the privacy in publication of high dimensional databases. We propose greedy approach, which scales much better and in most cases finds solution close to the optimal. The proposed algorithm is experimentally evaluated.