Abstract
Massive amounts of newly generated gene expression data have been used to further enhance personalised health predictions. Machine learning algorithms prepare techniques to explore a group of genes with similar profiles. Biclustering algorithms were proposed to resolve key issues of traditional clustering techniques and are well-adapted to the nature of biological processes. Besides, the concept of genome data access should be socially acceptable for patients since they can then be assured that their data analysis will not be harmful to their privacy and ultimately achieve good outcomes for society [1]. Homomorphic encryption has shown considerable potential in securing complicated machine learning tasks. In this paper, we prove that homomorphic encryption operations can be applied directly on biclustering algorithm (Cheng and Church algorithm) to process gene expression data while keeping private data encrypted. This Secure Cheng and Church algorithm (SeCCA) includes nine steps, each providing encryption for a specific section of the algorithm. Because of the current limitations of homomorphic encryption operations in real applications, only four steps of SeCCA are implemented and tested with adjustable parameters on a real-world data set (yeast cell cycle) and synthetic data collection. As a proof of concept, we compare the result of biclusters from the original Cheng and Church algorithm with SeCCA to clarify the applicability of homomorphic encryption operations in biclustering algorithms. As the first study in this domain, our study demonstrates the feasibility of homomorphic encryption operations in gene expression analysis to achieve privacy-preserving biclustering algorithms.
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VahidianSadegh, S., Wiese, L., Brenner, M. (2023). SeCCA: Towards Privacy-Preserving Biclustering Algorithm with Homomorphic Encryptions. In: Bieker, F., Meyer, J., Pape, S., Schiering, I., Weich, A. (eds) Privacy and Identity Management. Privacy and Identity 2022. IFIP Advances in Information and Communication Technology, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-031-31971-6_15
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DOI: https://doi.org/10.1007/978-3-031-31971-6_15
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