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Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) A Differential Privacy Approach to Preserve GWAS Data Sharing based on A Game Theoretic Perspective
¿µ¹®Á¦¸ñ(English Title) A Differential Privacy Approach to Preserve GWAS Data Sharing based on A Game Theoretic Perspective
ÀúÀÚ(Author) Jun Yan   Ziwei Han   Yihui Zhou1   Laifeng Lu  
¿ø¹®¼ö·Ïó(Citation) VOL 16 NO. 03 PP. 1028 ~ 1046 (2022. 03)
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(Korean Abstract)
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(English Abstract)
Genome-wide association studies (GWAS) aim to find the significant genetic variants for common complex disease. However, genotype data has privacy information such as disease status and identity, which make data sharing and research difficult. Differential privacy is widely used in the privacy protection of data sharing. The current differential privacy approach in GWAS pays no attention to raw data but to statistical data, and doesn¡¯t achieve equilibrium between utility and privacy, so that data sharing is hindered and it hampers the development of genomics. To share data more securely, we propose a differential privacy preserving approach of data sharing for GWAS, and achieve the equilibrium between privacy and data utility. Firstly, a reasonable disturbance interval for the genotype is calculated based on the expected utility. Secondly, based on the interval, we get the Nash equilibrium point between utility and privacy. Finally, based on the equilibrium point, the original genotype matrix is perturbed with differential privacy, and the corresponding random genotype matrix is obtained. We theoretically and experimentally show that the method satisfies expected privacy protection and utility. This method provides engineering guidance for protecting GWAS data privacy.
Å°¿öµå(Keyword) Genome-wide association studies   differential privacy   Nash equilibrium   chi-squared test  
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