Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Providing Database as a Service
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
Combining fragmentation and encryption to protect privacy in data storage
ACM Transactions on Information and System Security (TISSEC)
Protecting privacy in data release
Foundations of security analysis and design VI
Enforcing confidentiality and data visibility constraints: an OBDD approach
DBSec'11 Proceedings of the 25th annual IFIP WG 11.3 conference on Data and applications security and privacy
Private data indexes for selective access to outsourced data
Proceedings of the 10th annual ACM workshop on Privacy in the electronic society
Protecting privacy of sensitive value distributions in data release
STM'10 Proceedings of the 6th international conference on Security and trust management
Extending loose associations to multiple fragments
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Modeling and preventing inferences from sensitive value distributions in data release
Journal of Computer Security - STM'10
An OBDD approach to enforce confidentiality and visibility constraints in data publishing
Journal of Computer Security - DBSec 2011
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We propose a modeling of the problem of privacy-compliant data publishing that captures confidentiality constraints on one side and visibility requirements on the other side. Confidentiality constraints express the fact that some attributes, or associations among them, are sensitive and cannot be released. Visibility requirements express requests for views over data that should be provided. We propose a solution based on data fragmentation to split sensitive associations while ensuring visibility. In addition, we show how sensitive associations broken by fragmentation can be released in a sanitized form as loose associations formed in a way to guarantee a specified degree of privacy.