Privacy in Pharmacogenetics
 An End-to-End Case Study of Personalized Warfarin Dosing

1. PRIVACY IN PHARMACOGENETICS
AN END-TO-END CASE STUDY OF
PERSONALIZED WARFARIN DOSING
A REPORT WRITTEN BY:
AL ANOUD ALQOUFI- SHATAHA AL TALHI
KING SAUD UNIVERSITY
IS 563 INFORMATION SECURITY MANAGEMENT & AUDIT
SUPERVISED BY :
DR. MOHAMMED ALHUSSEIN

2. Outline
• Introduction
• Pharmacogenetic and Warfarin
• Model inversion
• Differential privacy
• Results
• Conclusion
• References

3. Introduction
A case study was introduced using warfarin dosage which is the most
used in pharmacogenetic modeling. researchers emphasized on the
amount of private information that might be revealed by model
inversion attacks .
Differential privacy was used for building pharmacogenetic models
The case study concluded that differential privacy cannot grantee privacy
protection without effecting clinical efficiency.

4. Pharmacogenetic & Warfarin
WarfarinIt is the most popular anticoagulant used to
decrease the possibility of heart attacks and strokes to
occur .
Low dose
Pharmacogenetic = Pharma + Genetic

5. Pharmacogenetic & Warfarin
Genotype
Clinical
variables
Trained
model
Medical
Guidance
Population
dataset
Trained
model
Learning
Algorithm

6. Linear Model f(x)
 Sqrt(dose) = 5.6044 + 0.2614 * age + 0.8677 * vkorc1=A/G
- 1.6974 * vkorc1=A/A - 1.9206 * cyp2c9=*2/*3 - 2.3312 *
cyp2c9=*3/*3 + 0.1092 * asian race - 0.2760 * black or
african american - ……

7. Model Inversion

8. Differential privacy
DP objective is to prevent attackers to conclude that a
subject was in the set used to construct a model or not.
Most DP mechanisms “Add noise” according to privacy
budget .
DP Insures patients’ privacy

9. Results

10. CONCLUSION
Current methods fail to balance privacy and utility in
Pharmacogenetic models

11. REFERENCES
1. M. Fredrikson, E. Lantz and S. Jha, "Privacy in pharmacogenetics: An end-to-end case study of personalized
warfarin dosing," in 23rd USENIX Security Symposium (USENIX Security 14), San Diego, 2014.
2. J. A. Johnson and d. L. H. Cavallari, "Warfarin pharmacogenetics," Trends in cardiovascular medicine 25, vol. 1,
pp. 33-41, 2015.
3. A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, Paris: Siam, 2005.
4. D. F. Kamal and K. E. Emam, "The application of differential privacy to health data.," in Proceedings of the
2012 Joint EDBT/ICDT Workshops.ACM, New York, 2012.
5. J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett. Functional mechanism: regression analysis under
differential privacy. In VLDB, 2012.
6. S. Vinterbo. Differentially private projected histograms: Construction and use for prediction. In ECML-PKDD,
2012.
7. D. Hand and R. Till. A simple generalization of the area under the ROC curve for multiple class classification
problems. Machine Learning, 45(2):171– 186, 2001.