基于经验小波变换的基因关联隐私保护实验研究

Privacy Protection Experimental Research on Genes Association Ranking Based on Empirical Wavelet Transform

为了解决某类风湿性关节炎与致病基因单核苷酸多态性(Single-Nucleotide Polymorphism,SNP)的相关度研究中,针对病人隐私保护强度与数据可用性的权衡问题,提出一种新型的基于经验小波变换(Empirical Wavelet Transform,EWT)的隐私保护方法.该方法通过对差分隐私加噪机制产生的数据进行EWT变换和分解,然后计算各EWT分量的峭度值并筛选出可能的噪声分量,去除一定的噪声分量后对信号进行重构得到新数据,基于该数据进行致病基因相关度排序.实验结果表明使用该方法能在保证差分隐私保护强度的情况下提高数据可用性,实现了隐私保护强度与数据可用性的合理权衡.

Due to privacy concerns in the genome-wide association studies of rheumatoid arthritis,there has been applying differential privacy to protect phenotype information(disease status)from being leaked while returning highly associated SNP(Single-Nucleotide Polymorphism).The trade-off between privacy protection intensity and data availability is a great problem.In order to solve the problem,a novel differential privacy protection method based on EWT(Empirical Wavelet Transform)was proposed.This method achieved the balance between privacy protection intensity and data availability by processing the noise introduced by differential privacy.Firstly,the data with differential privacy noise mechanism was processed by EWT approach;secondly,the kurtosis values of each EWT component were calculated,then some account of noise components was filtered out.At last,the data was reconstructed.After the above steps,the new data was obtained;it would be sorted according to the correlation degree of pathogenic genes.The experimental results show that the novel method can improve the data availability while ensuring the differential privacy protection intensity,and achieve a reasonable trade-off between the privacy protection intensity and the data availability.