Privacy in Pharmacogenetics: An End-to-End Case Study of … · 2016. 4. 30. · Warfarin Dosing Ø...
Transcript of Privacy in Pharmacogenetics: An End-to-End Case Study of … · 2016. 4. 30. · Warfarin Dosing Ø...
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Privacy in Pharmacogenetics: An End-to-End Case
Study of Personalized Warfarin Dosing
Presented By Sharani Sankaran
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Pharmacogenetics
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We Introduce an Attack called the Model Inversion Attack
Genomic privacy= Extract Patients Genetics from Pharmacogenetics Dosing Models
End-End Study- Differential Privacy Prevents the attack. Risk of Adverse Outcomes is too high with Differential Privacy
Current Method fails to balance privacy and utility which is a main concern when Inaccuracy is expensive
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Warfarin Dosing Ø Warfarin is the most popular
anticoagulant drug in use today.
Ø Anticoagulants are used to prevent stroke and other clotting related incidents.
Ø Warfarin is one of the oldest and well studied targets in pahrmacogenetics.
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• Warfarin is very difficult to prescribe doses for patients correctly.
• Low Dose High Dose
• Death Embolism Intracranial Bleeding Death
Stroke Extracranial Bleeding
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• Things Collected from each patient are • Age • Hieght Patients Demographics,relevant parts of their medical
history,comorbidities,smoking status . • Independent variables • weight • Age • Relevant Genotype : vkorc1,cyp2c9. • These 2 aspects of their genotype that researchers previously found
effect warfarin metabolism. • Target outcome: Stable Dosage of Warfarin that achieved optimal
therapeutic benefit for the patient. • The IWPC confirmed that ordinary linear regression is the best
learning algorithm y = ax + b
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• The algorithm for computing the likelihood is optimal with the given information given that it minimizes the misprediction rate for these missing medical history ,genotypes
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We Introduce an Attack called the Model Inversion Attack
Genomic privacy= Extract Patients Genetics from Pharmacogenetics Dosing Models
End-End Study- Differential Privacy Prevents the attack. Risk of Adverse Outcomes is too high with Differential Privacy
Current Method fails to balance privacy and utility which is a main concern when Inaccuracy is expensive
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Differential Privacy • Model Inversion is a problem so how to prevent it. • We examine how to use differential privacy to prevent model
inversion. • A computation is differentially private if any output it
produces going to be about as likely regardless of whether or not any particular individual row input to that computation.
• For D D' differing in one row • Pr[K(D) = s] <=exp(e) *Pr[K(D')=s] • Most Differential mechanism work by adding noise to their
output in some capacity according to privacy budget • There is also evidence of existing work that the attributes of
virtual linear models are trained to be protected by adding the noise to the coefficients of those linear models.
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Conclusion • Current Method fails to balance privacy and utility which is
main concern when Inaccuracy is expensive • This paper did not observe that a privacy budget significantly
prevented model inversion without introducing risk over fixed dosing.