ArticleProceedings of the ... USENIX Security Symposium. UNIX Security Symposium2014
Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
Article in Proceedings of the ... USENIX Security Symposium. UNIX Security Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed, 564 citations in OpenAlex.
- Aim high, stay private: differentially private synthetic data enables public release of behavioral health information with high utility.JAMIA open · 2026Article
- Enhanced model inversion via frequency disentanglement and latent space optimization.Scientific reports · 2025Article
- Generating synthetic genotypes using diffusion models.Bioinformatics (Oxford, England) · 2025Article
- Addressing contemporary threats in anonymised healthcare data using privacy engineering.NPJ digital medicine · 2025Review
- Integration of large language models and federated learning.Patterns (New York, N.Y.) · 2024Review
- Anonymization: The imperfect science of using data while preserving privacy.Science advances · 2024Review
- Privacy preservation for federated learning in health care.Patterns (New York, N.Y.) · 2024Review
- Correlation inference attacks against machine learning models.Science advances · 2024Article
- Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review.JMIR bioinformatics and biotechnology · 2024Article
- Evaluating the understanding of the ethical and moral challenges of Big Data and AI among Jordanian medical students, physicians in training, and senior practitioners: a cross-sectional study.BMC medical ethics · 2024Article
- PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data.Science advances · 2024Article
- A unified method to revoke the private data of patients in intelligent healthcare with audit to forget.Nature communications · 2023Article
- Enabling Health Data Sharing with Fine-Grained Privacy.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
- FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems.Entropy (Basel, Switzerland) · 2023Article
- Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning.Entropy (Basel, Switzerland) · 2023Article
- Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data.Radiographics : a review publication of the Radiological Society of North America, Inc · 2023Article
- Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.Heliyon · 2023Review
- Task-Specific Adaptive Differential Privacy Method for Structured Data.Sensors (Basel, Switzerland) · 2023Article
- A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things.Sensors (Basel, Switzerland) · 2023Review
- Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications.Applied intelligence (Dordrecht, Netherlands) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
We initiate the study of privacy in pharmacogenetics, wherein machine learning models are used to guide medical treatments based on a patient's genotype and background. Performing an in-depth case study on privacy in personalized warfarin dosing, we show that suggested models carry privacy risks, in particular because attackers can perform what we call
Identifiers
27077138PMC4827719W1473189865What OpenQuestion holds
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.