Tel Aviv Sorasky Medical Center, Tel Aviv 64239, Israel.
Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215.
Proc Natl Acad Sci U S A. 2023 Aug 15;120(33):e2304415120. doi: 10.1073/pnas.2304415120. Epub 2023 Aug 7.
Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical datasets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their datasets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological datasets that the toolset achieves high accuracy and practical performance, which scales well to larger datasets. As part of this work, we propose a cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.
真实世界的医疗保健数据共享对于构建更广泛和更大的临床数据集至关重要，这可能会改善临床决策研究和结果。利益相关者通常不愿意在无法保证患者隐私、对其数据集进行适当保护以及无法控制其数据使用的情况下共享数据。全同态加密（FHE）是一种加密功能，它可以通过在不进行中间解密的情况下对加密数据进行计算来解决这些问题，从而在不泄露原始数据的情况下获得分析结果。这项工作提出了一个使用多方 FHE 进行肿瘤学数据协作隐私保护分析的工具集。我们的工具集支持生存分析、逻辑回归训练和几种常见的描述性统计。我们使用肿瘤学数据集证明了该工具集实现了高精度和实际性能，并且可以很好地扩展到大数据集。作为这项工作的一部分，我们提出了一种用于多方 FHE 中交互式引导的加密协议，这具有独立的意义。我们开发的工具集是通用的，可以应用于其他协作医疗和医疗保健应用领域。
