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Privacy-preserving dataset combination and Lasso regression for healthcare predictions

机译：保留隐私数据集组合和保健预测的Lasso回归

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Recent developments in machine learning have shown its potential impact for clinical use such as risk prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA. As a concrete use-case, hospital Erasmus?MC and health insurance company Achmea have data on individuals in the city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact lifestyle factors for heart failure. However, privacy and confidentiality concerns make it unfeasible to exchange these data. This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus?MC are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties. We implement our secure solution and describe its performance and scalability: we can train a prediction model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods. This article shows that it is possible to combine datasets and train a Lasso regression model on this combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in the medical domain.
机译：机器学习的最新进程表明其对临床用途的潜在影响，例如风险预测，预后和治疗选择。但是，相关数据往往分散在不同的利益相关者身上，其使用受到调节，例如，通过gdpr或hipaa。作为一个具体用例，医院伊拉斯谟？MC和健康保险公司Achmea在鹿特丹市拥有数据，从理论上将使他们能够培训回归模型，以确定心力衰竭的高影响力因素。但是，隐私和保密性问题使得交换这些数据不可行。本文介绍了一种解决方案，其中achmea和erasmus的垂直分区合成数据使用安全的多方计算组合。首先，发生安全内连接协议以安全地确定在两个数据集中表示的患者的标识符。然后，在安全组合数据上培训安全的套索回归模型。因此，所涉及的各方获得预测模型，但没有关于其他方的输入数据的进一步信息。我们实施安全解决方案并描述其性能和可扩展性：我们可以在两个数据集中培训预测模型，每个数据集每次有5000条记录，总共30个功能在不到一个小时内，与标准结果的差异很小（非安全） 方法。本文展示可以以安全的方式将数据集和携带套索回归模型组合起来。因此，这种解决方案进一步扩展了医学领域中的隐私保留数据分析的可能性。

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