The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational disclosure control: a primer on data privacy protection
Computational disclosure control: a primer on data privacy protection
Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Privacy: A Machine Learning View
IEEE Transactions on Knowledge and Data Engineering
Preserving Privacy by De-Identifying Face Images
IEEE Transactions on Knowledge and Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
To do or not to do: the dilemma of disclosing anonymized data
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
Composition and Disclosure of Unlinkable Distributed Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Revisiting the uniqueness of simple demographics in the US population
Proceedings of the 5th ACM workshop on Privacy in electronic society
A computational model to protect patient data from location-based re-identification
Artificial Intelligence in Medicine
Towards an information theoretic metric for anonymity
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
ICDT'05 Proceedings of the 10th international conference on Database Theory
Measuring unlinkability revisited
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Secure construction of k-unlinkable patient records from distributed providers
Artificial Intelligence in Medicine
Cloning for privacy protection in multiple independent data publications
Proceedings of the 20th ACM international conference on Information and knowledge management
Data privacy against composition attack
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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In the past, data holders protected the privacy of their constituents by issuing separate disclosures of sensitive (e.g., DNA) and identifying data (e.g., names). However, individuals visit many places and their location-visit patterns, or ''trails'', can re-identify seemingly anonymous data. In this paper, we introduce a formal model of privacy protection, called k-unlinkability, to prevent trail re-identification in distributed data. The model guarantees that sensitive data trails are linkable to no less than k identities. We develop a graph-based model and illustrate how k-unlinkability is a more appropriate solution to this privacy problem compared to alternative privacy protection models.