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支持容错的轻量级可验证隐私保护传染病监测数据聚合方案

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随着各种流行传染病在全球频繁暴发,传染病监测在阻止传染病传播方面发挥着至关重要的作用。隐私保护数据聚合技术常用于避免传染病监测数据传输造成的用户隐私泄露问题。然而,现有的数据聚合方案仍然具有一些安全问题,如聚合节点不可信等。为了解决这些问题,本文提出了一个支持容错的轻量级可验证隐私保护传染病监测数据聚合方案。首先,使用基于CRT(Chinese remainder theorem)改进的Paillier同态加密系统和支持批量验证的签名算法分别对传染病数据进行高效加密和签名,以保护数据传输过程中的数据隐私和数据完整性。其次,使用承诺机制解决聚合节点不可信的问题。此外,本方案支持容错,即使某些用户和聚合节点没有按时地上传数据,聚合工作依然能够继续。特别地,本方案能够抵抗合谋攻击,满足更高的安全需求。由于本方案没有使用高耗时的计算操作,如双线性映射等,仿真实验证明本方案具有优秀的计算和通信开销,可以安全有效地应用于传染病检测系统。
Lightweight verifiable privacy-preserving infectious disease surveillance data aggregation scheme with fault tolerance
With frequent outbreaks of various epidemic infectious diseases across the globe,infectious disease surveillance plays a vital role in stopping the spread of infectious diseases.Privacy-preserving data aggregation is often used to avoid user privacy leakage caused by the transmission of infectious disease data.However,existing data aggregation schemes still have some security problems,such as untrusted aggregation nodes.To solve above problems,we propose a lightweight verifiable privacy-preserving infectious disease surveillance data aggregation scheme with fault tolerance.First,the improved Paillier homomorphic algorithm based on CRT and the signature algorithm with batch verification are used to efficiently encrypt and sign the infectious disease data to protect the data privacy and data integrity during data transmission.Second,the commitment mechanism is used to solve the problem of untrustworthiness of aggregate nodes.In addition,this scheme supports fault tolerance,and the aggregation work can continue even if some users and aggregation nodes do not upload data on time.In particular,this scheme can resist collusion attacks and meet higher security requirements.Since this scheme does not use time-consuming computational operations,such as bilinear mapping,simulation experiments show that the proposed scheme has excellent computational and communication overhead and can be safely and effectively applied to infectious disease surveillance systems.

infectious disease surveillancedata aggregationprivacy-preservinghomomorphiclightweight

杨小东、杨兰、魏丽珍、杜小妮、王彩芬

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西北师范大学计算机科学与工程学院,兰州 730070

西北师范大学数学与统计学院,兰州 730070

深圳技术大学大数据与网络学院,深圳 518118

传染病监测 数据聚合 隐私保护 同态加密 轻量级

2024

中国科学F辑
中国科学院,国家自然科学基金委员会

中国科学F辑

CSTPCD北大核心
影响因子:1.438
ISSN:1674-5973
年,卷(期):2024.54(11)