Privacy-Preserving Multiple Linear Regression of Vertically Partitioned Real Medical Datasets

By Hiroaki Kikuchi, Chika Hamanaga, Hideo Yasunaga, Hiroki Matsui, Hideki Hashimoto. 2017

23 citations recorded by the source corpus.

This paper studies the feasibility of privacy-preservingdata mining in epidemiological study. As for the data-miningalgorithm, we focus to a linear multiple regression thatcan be used to identify the most significant factorsamong many possible variables, such as the historyof many diseases. We try to identify the linear model to estimate a lengthof hospital stay from distributed dataset related tothe patient and the disease information. In this paper, we have done experiment usingthe real medical dataset related to stroke andattempt to apply multiple regression with sixpredictors of age, sex, the medical scales, e.g., Japan Coma Scale, and the modified Rankin Scale. Our contributions of this paper include(1) to propose a practical privacy-preserving protocols for linear multiple regressionwith vertically partitioned datasets, and(2) to show the feasibility of the proposed system usingthe real medical dataset distributed into two parties, the hospital who knows the technical details of diseasesduring the patients are in the hospital, and the local government who knows the residence even afterthe patients left hospital. (3) to show the accuracy and the performance of thePPDM system which allows us to estimate the expectedprocessing time with arbitrary number of predictors.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yang Liu
2. Tianjian Chen
3. Peng Liu
4. Zhi-qian Yi
5. Di Niu
6. Linglong Kong
7. Yaochen Hu
8. Graham Neubig

In Information Systems

1. Tianjian Chen
2. Peng Liu
3. Yang Liu

In Sociology and Political Science

1. Tianjian Chen
2. Peng Liu
3. Yang Liu

Citations in the corpus

• Backdoor attacks and defenses in feature-partitioned collaborative learning (2020, background): “, 2019), linear and logistic regression (Yang et al., 2019b; Hardy et al., 2017; Gratton et al., 2018; Kikuchi et al., 2017; Hu et al., 2019a), and neural networks (Liu et al.”
• Learning Privately over Distributed Features: An ADMM Sharing Approach (2019, background): “Another approach to privacy-preserving machine learning is through encryption Gilad-Bachrach et al. (2016); Takabi et al. (2016); Kikuchi et al. (2018) or secret sharing Mohassel and Zhang (2017); Wan et al. (2007); Bonte and Vercauteren (2018), so that models are trained on encrypted data.”