一种支持隐私保护的传染病人际传播分析模型

doi:10.3969/j.issn.1671-1122.2023.03.004

信息网络安全 ›› 2023, Vol. 23 ›› Issue (3): 35-44.doi: 10.3969/j.issn.1671-1122.2023.03.004

一种支持隐私保护的传染病人际传播分析模型

李晓华¹, 王苏杭²(), 李凯³, 徐剑²^,⁴

1.东北大学计算机科学与工程学院，沈阳110169
2.东北大学软件学院，沈阳 110169
3.国网新疆电力有限公司信息通信公司，乌鲁木齐 830002
4.中国科学院信息工程研究所信息安全国家重点实验室，北京 100093

收稿日期:2022-12-28 出版日期:2023-03-10 发布日期:2023-03-14
通讯作者: 王苏杭 E-mail:1058348091@qq.com
作者简介:李晓华（1969—），女，辽宁，副教授，博士，主要研究方向为信息安全与隐私保护|王苏杭（1997—），男，湖北，硕士研究生，主要研究方向为网络与信息安全|李凯（1988—）男，辽宁，高级工程师，硕士，主要研究方向为信息安全、电网数字化|徐剑（1978—），男，辽宁，教授，博士，主要研究方向为网络与信息安全
基金资助:
国家自然科学基金(61872069);国家自然科学基金(61991404);国家科技重大专项(J2019-IV-0002-0069)

A Privacy-Preserving Analysis Model of Human-to-Human Transmission of Infectious Diseases

LI Xiaohua¹, WANG Suhang²(), LI Kai³, XU Jian²^,⁴

1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
2. Software College, Northeastern University, Shenyang 110169, China
3. State Grid Xinjiang Information and Telecommunication Company, Urumqi 830002, China
4. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Received:2022-12-28 Online:2023-03-10 Published:2023-03-14
Contact: WANG Suhang E-mail:1058348091@qq.com

摘要/Abstract

摘要：

随着万物互联和大数据时代的到来，通过线下交互数据追踪传染病患者的密切接触者，利用健康数据对密切接触者的健康状态进行持续监测，为传染病人际传播分析带来了新的研究视角，为阻断传染病的传播提供了新的处理方式。然而，此类方法也存在较为严重的隐私泄露问题。为此，文章设计了基于线下交互和健康数据的传染病人际传播分析模型（Analysis Model of Human-to-Human Transmission of Infectious Diseases Based on Offline Interaction and Health Data，AMHHTID-OIHD）。该模型由可信机构、健康云服务器、交互云服务器、疾控中心、医院和用户6种实体组成，在支持隐私保护的同时实现CDC查找该患者的密切接触者并对其进行健康状态分类。文章以KNN分类和高斯朴素贝叶斯分类为基础，结合同态加密技术，设计了支持AMHHTID-OIHD的隐私保护密切接触者查找算法和隐私保护健康状态分类算法。最后，对该模型的安全性进行分析，结果表明该模型可以在保护隐私的情况下实现密切接触者查找和健康状态分类。

关键词: 密切接触者, 传染病, 隐私保护, 线下交互数据, 健康数据

Abstract:

With the advent of the Internet of Everything and the era of big data, tracking the close contacts of patients through offline interactive data, and using health data to continuously detect the health status of close contacts bring new research perspectives for the analysis of human-to-human transmission of infectious diseases and provid a new way of blocking the spread of infectious diseases. However, such methods also have serious privacy leakage problems. Therefore, an Analysis Model of Human-to-Human Transmission of Infectious Diseases based on Offline Interaction and Health Data (AMHHTID-OIHD) was designed based on offline interaction and health data. The model consisted of six entities: trusted institutions, health cloud servers, interactive cloud servers, Centers for Disease Control (CDC), hospitals, and users. Finally, CDC found close contacts of the patient and classifies their health status in privacy-preserving way. Based on KNN classification and Gaussian Naive Bayes classification, combined with homomorphic encryption technology, the ciphertext conversion algorithm, privacy protection close contact search algorithm, and privacy protection health state classification algorithm of AMHHTID-OIHD were designed. The correctness and safety of the above algorithms were also analyzed and tested. The test results show that our model can complete the expected task objectives with a low overhead and privacy protection.

Key words: close contacts, infectious disease, privacy-preserving, offline interaction, health data

中图分类号:

TP309

李晓华, 王苏杭, 李凯, 徐剑. 一种支持隐私保护的传染病人际传播分析模型[J]. 信息网络安全, 2023, 23(3): 35-44.

LI Xiaohua, WANG Suhang, LI Kai, XU Jian. A Privacy-Preserving Analysis Model of Human-to-Human Transmission of Infectious Diseases[J]. Netinfo Security, 2023, 23(3): 35-44.

图/表 4

参考文献 25

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一种支持隐私保护的传染病人际传播分析模型

A Privacy-Preserving Analysis Model of Human-to-Human Transmission of Infectious Diseases

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