# Quantifying Privacy Risks for Continuous Trait Data
## 3 Related Work  
In the context of the present paper, genomic privacy risk can be mainly divided into several scenarios, such as the \(n=1\)_scenario_, the _summary statistic scenario_, and the _gene expression scenario_, etc. **Studies in the \(n=1\) scenario**. In this scenario, the sensitive traits are correlated with individual's genotype directly [32]. Several attacks have been proposed for this scenario. [PERSON] et al. [33] discovered that approximately 45 carefully chosen SNPs could be used for major global population identification with a _type \(1\) error_ of \(10^{-15}\). More generally, [PERSON] et al. [34] summarized the influence of matching accuracy with the average MAF, number of SNPs and population, measured the TPR and FPR of predicitons correlated to these parameters in the \(n=1\)_scenario_, and revealed that a random subset of approximately 300 SNPs with an MAF \(\leqslant 0.1\) was sufficient to uniquely identify any person around the world. A more recent study on Beacon [35] demonstrate that only 1000 \"yes or no\" queries for SNPs can detect the presence of an individual from the Personal Genome Project [36]. To against this situation, in a study on methylation data sharing in Beacon, [PERSON] et al. [37] proposed the MBeacon system for private DNA methylation data sharing with a comparison test. **Studies in the summary statistic scenario**. In this scenarios, Compared with the allele frequencies of the general population, allele frequencies from the case group will be positively biased toward the target genotypes [32], which was investigated in a landmark study by [PERSON] et al. [38], who found that ADAD on GWAS data could be achievedonly with the study participants' allele frequencies. Subsequent researches expanded the scope of the vulnerabilities for summary statistics and analysed its mathematical properties [39; 40; 41]. **Studies in the gene expression scenario**. In this scenarios, gene expressions that are linked to a series of attributes, such as QTL data, could be used for genomic privacy breaching. As [PERSON] et al. mentioned [20], with a training step using a standard reference eQTL data set, a Bayesian approach could be used to calculate the probability distributions of the expression data with given genotypes. The researchers then matched the target genotype and tested the TPR and FPR of this matching. with the top 1000 _cis_ eQTLs, this ADAD technique had high accuracy in large-scale simulations. [PERSON] et al. [42] notably emphasized this scenario with specific methylation data. [PERSON] et al. [43] described the biomolecular mechanism by which methylation data could lead to a privacy risk by analysing real data. Subsequently, [PERSON] et al. [21] used the Bayesian method adapted from [PERSON]'s work [20] to correlate the methylation profile to an individual genotype, which had an up to 97.5% matching accuracy with 293 independent meQTL-methylation pairs and without false positive predictions on a dataset of nearly 3000 samples. They also proved that the continuous distribution function could be best fixed as a normal distribution, which supports the findings of our simulation experiments. [PERSON] et al. [23] used 1062 volunteer samples for analysis and found that the associations between some common phenotypes and genes could lead to privacy threats. The findings of a more resent study from [PERSON] et al. [22], who used high-resolution 3D facial data and correlated them with SNP profiles, was challenged by [PERSON] et al. [44] in terms of the actual feasibility. **Studies in the other scenarios**. Regarding genomic privacy breaches, several papers have studied privacy risks related to various sorts of additional data or in spatial conditions with large-scale spatio-temporal extents. [PERSON] et al. showed that genotypes can be reidentified with genetic traits on the Y chromosome by querying genetic genealogy databases [45]. [PERSON] et al. showed that SNPs can also be exploited to further reidentify anonymous genotypes by typically using side channels [14].