# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 3 Algorithms
### Improvement of Secure CWC
Thus, the application and speedup of TFHE to ML is a promising research area for the future. In conclusion, it should be noted that with the development of FHE, practical algorithms for more challenging problems such as large-scale genome analysis (GWAS) [42; 43] and deep learning [44; 45; 46] are emerging. Conceptualization, H.S. ; methodology, T.I., K.S. and H.S. ; software, S.O., J.T. and M.K. ; validation, S.O., J.T. and M.K. ; formal analysis, T.I., K.S. and H.S. ; investigation, T.I., K.S. and H.S. ; resources, S.O., J.T. and M.K. ; data curation, S.O., J.T. and M.K. ; writing--original draft preparation, T.I., K.S. and H.S. ; writing--review and editing, T.I., K.S. and H.S. ; visualization, H.S. ; supervision, H.S. ; project administration, H.S. ; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript. This research was funded in part by JSPS KAKENHI (Grant Number 21H05052, 18H04098). Not applicable. The authors declare no conflict of interest. ## References

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