可穿戴设备的数值型流数据差分隐私均值发布

doi:10.11772/j.issn.1001-9081.2019111929

计算机应用 ›› 2020, Vol. 40 ›› Issue (6): 1692-1697.DOI: 10.11772/j.issn.1001-9081.2019111929

可穿戴设备的数值型流数据差分隐私均值发布

涂子璇, 刘树波, 熊星星, 赵晶, 蔡朝晖

武汉大学计算机学院，武汉 430072

收稿日期:2019-11-13 修回日期:2020-03-05 出版日期:2020-06-10 发布日期:2020-06-18
通讯作者: 刘树波(1970—)
作者简介:涂子璇(1996—)，女，湖北黄冈人，硕士研究生，主要研究方向：信息安全、差分隐私.刘树波(1970—)，男，黑龙江泰来人，教授，博士生导师，博士，主要研究方向：物联网、嵌入式系统.熊星星(1989—)，男，江西南昌人，博士研究生，主要研究方向：信息安全、差分隐私.赵晶(1996—)，男，内蒙古巴彦淖尔人，硕士研究生，主要研究方向：信息安全、差分隐私.蔡朝晖(1968—)，女，湖北黄冈人，副教授，博士，主要研究方向：分布式信息处理.

Differential private average publishing of numerical stream data for wearable devices

TU Zixuan, LIU Shubo, XIONG Xingxing, ZHAO Jing, CAI Zhaohui

School of Computer Science, Wuhan University, Wuhan Hubei 430072, China

Received:2019-11-13 Revised:2020-03-05 Online:2020-06-10 Published:2020-06-18
Contact: LIU Shubo, born in 1970, Ph. D., professor. His research interests include Internet of things, embedded system.
About author:TU Zixuan, born in 1996, M. S. candidate. Her research interests include information security, differential privacy.LIU Shubo, born in 1970, Ph. D., professor. His research interests include Internet of things, embedded system.XIONG Xingxing, born in 1989, Ph. D. candidate. His research interests include information security, differential privacy.ZHAO Jing, born in 1996, M. S. candidate. His research interests include information security, differential privacy.CAI Zhaohui, born in 1968, Ph. D., associate professor. Her research interests include distributed information processing.
Supported by:
Applied Basic Research Program of Wuhan (2017060201010162), the Major Technical Innovation Project of Hubei Province (2018AAA046), the National Natural Science Foundation of China (61872431).

摘要/Abstract

摘要： 可穿戴设备实时产生的用户健康数据（如心率、血糖等）对健康监测及疾病诊断具有重大意义，然而健康数据属于用户的隐私信息。针对可穿戴设备的数值型流数据均值发布，为防止用户的隐私信息泄漏，提出一种基于自适应采样的可穿戴设备差分隐私均值发布方法。首先，引入适应可穿戴设备流数据均值波动小这一特点的全局敏感度；然后，采用基于卡尔曼滤波调整误差的自适应采样的方式分配隐私预算，提高发布数据的可用性。在发布两种健康数据的实验中，所提方法在隐私预算为0.1时，即高隐私保护强度下，在心率和血糖数据集上的平均相对误差(MRE)分别为0.01和0.08，相较于差分隐私时序监测的滤波和自适应采样（FAST）算法分别降低了36%和33%。所提的均值发布方法能够提高可穿戴设备均值流数据发布的可用性。

关键词: 可穿戴设备, 差分隐私, 流数据发布, 均值统计, 自适应采样

Abstract: User health data such as heart rate and blood glucose generated by wearable devices in real time is of great significance for health monitoring and disease diagnosis. However, health data is private information of users. In order to publish the average value of numerical stream data for wearable devices and prevent the leakage of users’ privacy information, a new differential private average publishing method of wearable devices based on adaptive sampling was proposed. Firstly, the global sensitivity was introduced which was adaptive to the characteristic of small fluctuation of stream data average for wearable devices. Then, the privacy budget was allocated by the adaptive sampling based on Kalman filter error adjustment, so as to improve the availability of the published data. In the experiments of two kinds of health data publishing, while the privacy budget is 0.1, which means that the level of privacy protection is high, the Mean Relative Errors (MRE) of the proposed method on the heart rate dataset and blood glucose dataset are only 0.01 and 0.08, which are 36% and 33% lower than those of Filtering and Adaptive Sampling for differential private Time-series monitoring (FAST) algorithm. The proposed method can improve the usability of wearable devices’ stream data publishing.

Key words: wearable device, differential privacy, stream data publishing, average statistics, adaptive sampling

中图分类号:

TP309.2

涂子璇, 刘树波, 熊星星, 赵晶, 蔡朝晖. 可穿戴设备的数值型流数据差分隐私均值发布[J]. 计算机应用, 2020, 40(6): 1692-1697.

TU Zixuan, LIU Shubo, XIONG Xingxing, ZHAO Jing, CAI Zhaohui. Differential private average publishing of numerical stream data for wearable devices[J]. Journal of Computer Applications, 2020, 40(6): 1692-1697.

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可穿戴设备的数值型流数据差分隐私均值发布

Differential private average publishing of numerical stream data for wearable devices

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