# Quantifying Privacy Risks for Continuous Trait Data
###### Abstract  
In the context of life sciences, the rapid biotechnological development leads to the creation of huge amounts of biological data. The use of such data naturally brings concerns on human genetic privacy breaches, which also discourage biological data sharing. Prior studies have investigated the possibility of the privacy issues associated with individuals' trait data. However, there are few studies on quantitatively analyzing the probability of the privacy risk. In this paper, we fill this void by proposing a scheme for systematically breaching genomic privacy, which is centered on quantifying the probability of the privacy risk of continuous trait data. With well-designed synthetic datasets, our theoretical analysis and experiments lead to several important findings, such as: (i) The size of genetic signatures and the _sensitivity_ (true positive rate) significantly affect the accuracy of re-identification attack. (ii) Both the size of genetic signatures and the minor allele frequency have a significant impact on distinguishing true positive and false positive matching between traits and genetic profiles. (iii) The size of the matching quantitative trait locus dataset has a large impact on the confidence of the privacy risk assessment. Validation with a real dataset shows that our findings can effectively estimate the privacy risks of the continuous trait dataset. genomic privacy; re-identification; quantitative trait locus; sensitivity +
Footnote †: journal: Medical  
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by matching anonymized medical data with public genomic data [16; 17], genomic privacy breaches will have long-term adverse effects on individuals and their families. For the reasons above, both the executive branch and academia have paid increasing attention to genomic privacy. The US health care system has established strict privacy rules for the usage of genomic data [18], while the community website GenomePrivacy.org integrate genome privacy and security information for academic studies [19]. On the other hand, academic research has identified genomic privacy breaches or reidentification risks [20; 21; 22]. Specifically, [PERSON] et al. [20] used the top 1000 _cis_ eQTLs for reidentification on an expression database from the entire US population and achieved a Type 1 error of \(10^{-5}\) (wrongly identifying an innocent individual as a victim) and an accuracy of 85%. [PERSON] et al. [21] achieved a reidentification accuracy of 97.5% by relating DNA methylation data with genomic methylation Single Nucleotide Polymorphism (SNP) data. [PERSON] et al. [22] designed face-to-DNA classifiers to associate face images and genomic data, demonstrating substantial true matching (training accuracy 83%, verification accuracy 80%). However, previous studies only focus on qualitatively analyzing whether potential re-identification risks exist, but few studies have quantitatively analyzed under what circumstances re-identification risks exist. [PERSON] et al. [20], [PERSON] et al. [23], and [PERSON] et al. [21] focused on the possibility of privacy threat between DNA and phenotypes; all of their quantitative analyses were intended to illustrate the existence of the privacy threat, with little mention of how serious the privacy risks was in different situations. Furthermore, all the previous studies are limited to a certain class of continuous trait data. [PERSON] et al. [23] limited their quantitative models for just a few common phenotypes, [PERSON] et al. [20] only verified the privacy risks of RNA expression data, [PERSON] et al. [21] only estimated identification risks of methylation data with small overlap between distributions. In general, previous works had not proposed a method to predict the privacy risk of any set of continuous traits. **Contributions**.