论文标题
具有良好品味的隐私：量子研究量化遗传评分的隐私风险
Privacy with Good Taste: A Case Study in Quantifying Privacy Risks in Genetic Scores
论文作者
论文摘要
对遗传数据的分析为医学和科学进步开辟了许多机会。表型信息和多基因风险评分分析遗传数据的使用是广泛的。遗传隐私的大多数工作都集中在基本的遗传数据上，例如SNP值和特定的基因型。在本文中，我们引入了一种新颖的方法，以通过关注多基因分数和表型信息来量化和防止隐私风险。我们的方法基于工具支持的隐私风险分析方法Privug。我们证明了使用Privug来评估由TAS2R38和TAS2R16编码的苦味受体的多基因性状评分所带来的隐私风险，以在其种族的情况下对一个人的隐私。我们为遗传数据披露的不同程序提供广泛的隐私风险分析：品尝表型，品尝多基因评分和随噪声扭曲的多基因评分。最后，我们讨论多基因分数的隐私/公用事业权衡。
Analysis of genetic data opens up many opportunities for medical and scientific advances. The use of phenotypic information and polygenic risk scores to analyze genetic data is widespread. Most work on genetic privacy focuses on basic genetic data such as SNP values and specific genotypes. In this paper, we introduce a novel methodology to quantify and prevent privacy risks by focusing on polygenic scores and phenotypic information. Our methodology is based on the tool-supported privacy risk analysis method Privug. We demonstrate the use of Privug to assess privacy risks posed by disclosing a polygenic trait score for bitter taste receptors, encoded by TAS2R38 and TAS2R16, to a person's privacy in regards to their ethnicity. We provide an extensive privacy risks analysis of different programs for genetic data disclosure: taster phenotype, tasting polygenic score, and a polygenic score distorted with noise. Finally, we discuss the privacy/utility trade-offs of the polygenic score.
