ArticleJournal of the American Medical Informatics Association : JAMIA
Evaluating re-identification risks with respect to the HIPAA privacy rule.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
119 citing papers in PubMed.
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- An integrated evaluation framework for synthetic clinical data in severely imbalanced settings: fidelity, privacy-risk profiling, and diagnostic utility.BMC medical informatics and decision making · 2026Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Article
- A novel analysis methodology for assessment of re-identification risks for the National Cancer Institute cancer registry privacy preserving record linkage technique.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A 7-criteria evaluation approach for the responsible use of synthetic medical data.BJR artificial intelligence · 2026Article
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
- Practical and ready-to-use methodology to assess the re-identification risk in anonymized datasets.Scientific reports · 2025Article
- The Honest Enterprise Research Broker: Facilitating Ethical, Efficient, and Secure Access to Health Data for Research.Applied clinical informatics · 2025Article
- Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2025Review
- Ethical Dimensions of Clinical Data Sharing by U.S. Health Care Organizations for Purposes beyond Direct Patient Care: Interviews with Health Care Leaders.Applied clinical informatics · 2025Article
- Economics and Equity of Large Language Models: Health Care Perspective.Journal of medical Internet research · 2024Article
- Reliable generation of privacy-preserving synthetic electronic health record time series via diffusion models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- What Do We Mean by Sharing of Patient Data? DaSH: A Data Sharing Hierarchy of Privacy and Ethical Challenges.Applied clinical informatics · 2024Article
- Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.Statistics in biosciences · 2024Article
- A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Article
- Ethical Imperatives for Working With Diverse Populations in Digital Research.Journal of medical Internet research · 2023Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
objectiveMany healthcare organizations follow data protection policies that specify which patient identifiers must be suppressed to share "de-identified" records. Such policies, however, are often applied without knowledge of the risk of "re-identification". The goals of this work are: (1) to estimate re-identification risk for data sharing policies of the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule; and (2) to evaluate the risk of a specific re-identification attack using voter registration lists. MEASUREMENTS: We define several risk metrics: (1) expected number of re-identifications; (2) estimated proportion of a population in a group of size g or less, and (3) monetary cost per re-identification. For each US state, we estimate the risk posed to hypothetical datasets, protected by the HIPAA Safe Harbor and Limited Dataset policies by an attacker with full knowledge of patient identifiers and with limited knowledge in the form of voter registries.
resultsThe percentage of a state's population estimated to be vulnerable to unique re-identification (ie, g=1) when protected via Safe Harbor and Limited Datasets ranges from 0.01% to 0.25% and 10% to 60%, respectively. In the voter attack, this number drops for many states, and for some states is 0%, due to the variable availability of voter registries in the real world. We also find that re-identification cost ranges from $0 to $17,000, further confirming risk variability.
conclusionsThis work illustrates that blanket protection policies, such as Safe Harbor, leave different organizations vulnerable to re-identification at different rates. It provides justification for locally performed re-identification risk estimates prior to sharing data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.