PRIVACY IN PHARMACOGENETICS AN END-TO-END CASE STUDY OF

PERSONALIZED WARFARIN DOSINGA REPORT WRITTEN BY:

AL ANOUD ALQOUFI- SHATAHA AL TALHI

 

KING SAUD UNIVERSITY

IS 563 INFORMATION SECURITY MANAGEMENT &

AUDIT

SUPERVISED BY : DR. MOHAMMED ALHUSSEIN

Outline

• Introduction

• Pharmacogenetic and Warfarin

• Model inversion

• Differential privacy

• Results

• Conclusion

• References

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.

Pharmacogenetic & Warfarin

Warfarin It is the most popular anticoagulant used to decrease the possibility of heart attacks and strokes to occur .

Low dose

Pharmacogenetic = Pharma + Genetic

Pharmacogenetic & Warfarin

Genotype

Clinical variables

Trained model

Medical Guidance

Population dataset

Trained model

Learning Algorithm

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 - ……

Model Inversion

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

Results

CONCLUSION

Current methods fail to balance privacy and utility in Pharmacogenetic models

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.