Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing
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Summary
It is concluded that current DP mechanisms do not simultaneously improve genomic privacy while retaining desirable clinical efficacy, highlighting the need for new mechanisms that should be evaluated in situ using the general methodology introduced by this work.
- Type
- article
- Published
- 2014-08-01
- Cited by
- 913
- References
- 50
- Access
- Open access
- OpenAlex
- https://openalex.org/W1473189865
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2148083
Keywords
Dosing, Pharmacogenetics, Warfarin, Computer science, Personalized medicine
References
- Simple Demographics Often Identify People Uniquely
- Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data
- Estimation of Treatment Effects from Combined Data: Identification versus Data Security
- Clinical Trial Simulation in Drug Development
- The Power of Linear Reconstruction Attacks
- A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
- Genotypes of the cytochrome p450 isoform, CYP2C9, and the vitamin K epoxide reductase complex subunit 1 conjointly determine stable warfarin dose: a prospective study
- Clinical Trial Simulation: A Review
- The cost of privacy: destruction of data-mining utility in anonymized data publishing
- Comparison of 10-mg and 5-mg Warfarin Initiation Nomograms Together with Low-Molecular-Weight Heparin for Outpatient Treatment of Acute Venous Thromboembolism
- Clinical and Epidemiologic Features of Primary HIV Infection
- Differential privacy for collaborative security
- A Genome-Wide Association Study Confirms VKORC1, CYP2C9, and CYP4F2 as Principal Genetic Determinants of Warfarin Dose
- On the rationale of maximum-entropy methods
- Current Status of the International Normalized Ratio
- Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays
- The Promise of Differential Privacy: A Tutorial on Algorithmic Techniques
- No free lunch in data privacy
- A Regulatory Science Perspective on Warfarin Therapy: A Pharmacogenetic Opportunity
- Personal privacy vs population privacy: learning to attack anonymization
Cited by
- Towards Making Systems Forget with Machine Unlearning
- GenoGuard: Protecting Genomic Data against Brute-Force Attacks
- Space-Crossing: Community-Based Data Forwarding in Mobile Social Networks Under the Hybrid Communication Architecture
- Privacy in the Genomic Era
- Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies
- Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
- The effect of quasi-identifier characteristics on statistical bias introduced by k-anonymization
- Differentially Private Algorithms for Empirical Machine Learning
- Design of Policy-Aware Differentially Private Algorithms
- Privately Information Sharing with Delusive Paths for Data Forwarding in Vehicular Networks
- Principled Evaluation of Differentially Private Algorithms using DPBench
- Reassembling Our Digital Selves
- Privacy-preserving microbiome analysis using secure computation
- Quantification of Private Information Leakage from Phenotype-Genotype Data: Linking Attacks
- Regression Model Fitting under Differential Privacy and Model Inversion Attack
- Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
- Efficient differentially private learning improves drug sensitivity prediction
- Optimizing secure classification performance with privacy-aware feature selection
- Publishing Graph Degree Distribution with Node Differential Privacy
- Oblivious Multi-Party Machine Learning on Trusted Processors
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