# Quantifying Privacy Risks for Continuous Trait Data
## 8 Conclusions  
We have presented a re-identification attack via simulated genotype data and showed that the matching SNP size is one of the major factors that can be used to quantify both genomic privacy breaching accuracy and the discrimination rate of TP and FP matching.  
Then, by implementing a mathematical analysis of the distribution characteristics of our synthetic data, we demonstrated that the _sensitivity_, which is the main factor affecting the matching accuracy, is dependent on the ratio of standard deviation, the relative distance of expectation, and the ratio of prior probability, between different distributions with the same trait. Different from _sensitivity_, the MAF value is the main factor affecting the discrimination rate. Additionally, the size of the matching set will significantly affect the evaluation accuracy of privacy concerns in practical situations.  
Finally, a matching test with real data led to the conclusion that our evaluation criteria are effective in predicting the privacy risks of gene-related continuous trait data.  
Although our approach to assessing the privacy implications of continuous trait datasets has been shown to be effective, the present study has the following limitations, which need to be addressed in future studies.  
First, the parameter setting of the simulated data needs to be more refined. Ratios between standard deviation \(r_{a}\) and \(r_{\beta}\) should be more specific. Furthermore, since the distributions of \(r_{a}\) and \(r_{\beta}\) were not specified when the data had been generated, the matching accuracy in Figures 2 and 3 did not decrease monotonically with the increase of \(n\) and \(z\) but fluctuates, which was more likely to be related to the random distribution of \(r_{a}\) and \(r_{\beta}\). For the further studies, we will focus on analyzing how different \(r\) will affect the _sensitivity score_ and the effectiveness of the privacy breaching.  
Second, sampling method for synthetic data generation needs to be improved. 50 times for sampling and training is inability to show hidden dangers accurately when faced with smaller MAFs. In the following studies we will increase the number of samples and increase the sample size of the training set. Furthermore, we will expand the value range of parameters to know under what conditions the privacy breaching attack can achieve the effect of the real data used in the prevous work [20; 21].  
Finally, the method of calculating the _sensitivity score_\(\text{Sen}(i)\) that for the ternary classification needs to be improved. The existing solution only considers the impact of the lowest Sen on the matching accuracy, but in actual cases, the impact of the second lowest Sen needs to be validated. In future studies we will estimate the impact of the second lowest Sen with more refined parameter settings.  
Conceptualization, M.H.; Data curation, M.H.; Formal analysis, M.H. and W.W.; Investigation, M.H.; Methodology, S.X.; Project administration, W.Q. and H.J.; Resources, D.Z., W.Q. and H.J.; Supervision, D.Z.; Visualization, S.X.; Writing--original draft, M.H.; Writing--review & editing, S.X. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.