Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Practical Data-Oriented Microaggregation for Statistical Disclosure Control
IEEE Transactions on Knowledge and Data Engineering
A critique of the sensitivity rules usually employed for statistical table protection
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
On Privacy-Preserving Access to Distributed Heterogeneous Healthcare Information
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 6 - Volume 6
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
A distributed architecture for scalable private RFID tag identification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Generating microdata with p-sensitive k-anonymity property
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
Privacy protection in location-based services through a public-key privacy homomorphism
EuroPKI'07 Proceedings of the 4th European conference on Public Key Infrastructure: theory and practice
An Anonymity Model Achievable Via Microaggregation
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Genetic algorithm-based clustering approach for k-anonymization
Expert Systems with Applications: An International Journal
Systematic clustering method for l-diversity model
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Privacy beyond single sensitive attribute
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
An information theoretic privacy and utility measure for data sanitization mechanisms
Proceedings of the second ACM conference on Data and Application Security and Privacy
A classification of location privacy attacks and approaches
Personal and Ubiquitous Computing
Multivariate microaggregation by iterative optimization
Applied Intelligence
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Micro-data protection is a hot topic in the field of Statistical Disclosure Control (SDC), that has gained special interest after the disclosure of 658000 queries by the AOL search engine in August 2006. Many algorithms, methods and properties have been proposed to deal with micro-data disclosure, p-Sensitive k-anonymity has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p different values for each confidential attribute within the records sharing a combination of key attributes. Like k-anonymity, the algorithm originally proposed to achieve this property was based on generalisations and suppressions; when data sets are numerical this has several data utility problems, namely turning numerical key attributes into categorical, injecting new categories, injecting missing data, and so on. In this article, we recall the foundational concepts of micro-aggregation, k-anonymity and p-sensitive k-anonymity. We show that k-anonymity and p-sensitive k-anonymity can be achieved in numerical data sets by means of micro-aggregation heuristics properly adapted to deal with this task. In addition, we present and evaluate two heuristics for p-sensitive k-anonymity which, being based on micro-aggregation, overcome most of the drawbacks resulting from the generalisation and suppression method.