For the Record: Protecting Electronic Health Information
For the Record: Protecting Electronic Health Information
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Editorial: why HCI research in privacy and security is critical now
International Journal of Human-Computer Studies - Special isssue: HCI research in privacy and security is critical now
Usable privacy and security for personal information management
Communications of the ACM - Personal information management
ACM SIGKDD Explorations Newsletter
Evaluating interfaces for privacy policy rule authoring
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Biomedical Informatics
A computational model to protect patient data from location-based re-identification
Artificial Intelligence in Medicine
Towards the security and privacy analysis of patient portals
ACM SIGBED Review - Special issues on the NSF team for research in ubiquitous secure technology (TRUST) project reports
k-Unlinkability: A privacy protection model for distributed data
Data & Knowledge Engineering
Online privacy control via anonymity and pseudonym: Cross-cultural implications
Behaviour & Information Technology
Translational integrity and continuity: Personalized biomedical data integration
Journal of Biomedical Informatics
Formal anonymity models for efficient privacy-preserving joins
Data & Knowledge Engineering
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Secure construction of k-unlinkable patient records from distributed providers
Artificial Intelligence in Medicine
Strategies for health data exchange for secondary, cross-institutional clinical research
Computer Methods and Programs in Biomedicine
Uniqueness and how it impacts privacy in health-related social science datasets
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Messin' with texas deriving mother's maiden names using public records
ACNS'05 Proceedings of the Third international conference on Applied Cryptography and Network Security
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
Keeping Found Things Found: The Study and Practice of Personal Information Management: The Study and Practice of Personal Information Management
The effects of location access behavior on re-identification risk in a distributed environment
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
Improvements on a privacy-protection algorithm for DNA sequences with generalization lattices
Computer Methods and Programs in Biomedicine
Participatory personal data: An emerging research challenge for the information sciences
Journal of the American Society for Information Science and Technology
TrustBus'07 Proceedings of the 4th international conference on Trust, Privacy and Security in Digital Business
Studying genotype-phenotype attack on k-anonymised medical and genomic data
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Journal of Biomedical Informatics
Protecting and evaluating genomic privacy in medical tests and personalized medicine
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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The increasing integration of patient-specific genomic data into clinical practice and research raises serious privacy concerns. Various systems have been proposed that protect privacy by removing or encrypting explicitly identifying information, such as name or social security number, into pseudonyms. Though these systems claim to protect identity from being disclosed, they lack formal proofs. In this paper, we study the erosion of privacy when genomic data, either pseudonymous or data believed to be anonymous, are released into a distributed healthcare environment. Several algorithms are introduced, collectively called RE-Identification of Data In Trails (REIDIT), which link genomic data to named individuals in publicly available records by leveraging unique features in patient-location visit patterns. Algorithmic proofs of re-identification are developed and we demonstrate, with experiments on real-world data, that susceptibility to re-identification is neither trivial nor the result of bizarre isolated occurrences. We propose that such techniques can be applied as system tests of privacy protection capabilities.