Private pathological assessment via machine learning and homomorphic encryption

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-09-10 DOI:10.1186/s13040-024-00379-9
Ahmad Al Badawi, Mohd Faizal Bin Yusof
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Abstract

The objective of this research is to explore the applicability of machine learning and fully homomorphic encryption (FHE) in the private pathological assessment, with a focus on the inference phase of support vector machines (SVM) for the classification of confidential medical data. A framework is introduced that utilizes the Cheon-Kim-Kim-Song (CKKS) FHE scheme, facilitating the execution of SVM inference on encrypted datasets. This framework ensures the privacy of patient data and negates the necessity of decryption during the analytical process. Additionally, an efficient feature extraction technique is presented for the transformation of medical imagery into vectorial representations. The system’s evaluation across various datasets substantiates its practicality and efficacy. The proposed method delivers classification accuracy and performance on par with traditional, non-encrypted SVM inference, while upholding a 128-bit security level against established cryptographic attacks targeting the CKKS scheme. The secure inference process is executed within a temporal span of mere seconds. The findings of this study underscore the viability of FHE in enhancing the security and efficiency of bioinformatics analyses, potentially benefiting fields such as cardiology, oncology, and medical imagery. The implications of this research are significant for the future of privacy-preserving machine learning, promoting progress in diagnostic procedures, tailored medical treatments, and clinical investigations.
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通过机器学习和同态加密进行私人病理评估
本研究的目的是探索机器学习和全同态加密(FHE)在私密病理评估中的适用性,重点是用于机密医疗数据分类的支持向量机(SVM)的推理阶段。本文介绍了一种利用 Cheon-Kim-Kim-Song (CKKS) FHE 方案的框架,该框架有助于在加密数据集上执行 SVM 推断。该框架确保了患者数据的隐私性,并消除了分析过程中解密的必要性。此外,还介绍了一种高效的特征提取技术,用于将医学图像转换为矢量表示。该系统在各种数据集上的评估证明了其实用性和有效性。所提出的方法在分类准确性和性能上与传统的非加密 SVM 推理不相上下,同时还具有 128 位的安全级别,可抵御针对 CKKS 方案的加密攻击。安全推理过程的执行时间跨度仅为几秒钟。这项研究的发现强调了 FHE 在提高生物信息学分析的安全性和效率方面的可行性,可能会使心脏病学、肿瘤学和医学影像等领域受益。这项研究对保护隐私的机器学习的未来意义重大,可促进诊断程序、定制医疗和临床研究的进步。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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