Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Revisiting the uniqueness of simple demographics in the US population
Proceedings of the 5th ACM workshop on Privacy in electronic society
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Yet another privacy metric for publishing micro-data
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Privacy-Aware Biometrics: Design and Implementation of a Multimodal Verification System
ACSAC '08 Proceedings of the 2008 Annual Computer Security Applications Conference
Integrating private databases for data analysis
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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In modern digital society, personal information about individuals can be easily collected, shared, and disseminated. These data collections often contain sensitive information, which should not be released in association with respondents' identities. Removing explicit identifiers before data release does not offer any guarantee of anonymity, since deidentified datasets usually contain information that can be exploited for linking the released data with publicly available collections that include respondents' identities. To overcome these problems, new proposals have been developed to guarantee privacy in data release. In this chapter, we analyze the risk of disclosure caused by public or semi-public microdata release and we illustrate the main approaches focusing on protection against unintended disclosure. We conclude with a discussion on some open issues that need further investigation.