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Title:
Privacy-Preserving Federated Inference for Genomic Analysis with Homomorphic Encryption
Authors: Anish Chakraborty, Nektarios Georgios Tsoutsos
Abstract:In recent years, federated learning has gained significant momentum as a collaborative machine learning approach, particularly in the field of medicine. While the decentralized nature of federated learning boasts greater security guarantees compared to traditional machine learning methods, it is still susceptible to myriad attacks. Moreover, as federated learning becomes increasingly ubiquitous in medicine, its use for classification tasks is expected to increase; however, maintaining patient data confidentiality remains a significant challenge, especially for genetic data. In this work, we introduce a novel framework for secure federated inference on nucleotide-based genotype data and provide a gateway to private inference through fully homomorphic encryption. A federated model with five local clients was created and trained before being encrypted with the TFHE cryptosystem and placed for inference. This framework successfully identified promoter sequences encoded within given DNA sequences, showing its potential applications in secure genomic data analysis in a federated context. Our work represents a crucial step in privacy-preserving federated inference on nucleotide-based data.
ePrint: https://eprint.iacr.org/2025/1515
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