Zhu Hui, Gao Lijuan, Li Hui
Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2016 Feb 1;16(2):179. doi: 10.3390/s16020179.
With the development of body sensor networks and the pervasiveness of smart phones, different types of personal data can be collected in real time by body sensors, and the potential value of massive personal data has attracted considerable interest recently. However, the privacy issues of sensitive personal data are still challenging today. Aiming at these challenges, in this paper, we focus on the threats from telemetry interface and present a secure and privacy-preserving body sensor data collection and query scheme, named SPCQ, for outsourced computing. In the proposed SPCQ scheme, users' personal information is collected by body sensors in different types and converted into multi-dimension data, and each dimension is converted into the form of a number and uploaded to the cloud server, which provides a secure, efficient and accurate data query service, while the privacy of sensitive personal information and users' query data is guaranteed. Specifically, based on an improved homomorphic encryption technology over composite order group, we propose a special weighted Euclidean distance contrast algorithm (WEDC) for multi-dimension vectors over encrypted data. With the SPCQ scheme, the confidentiality of sensitive personal data, the privacy of data users' queries and accurate query service can be achieved in the cloud server. Detailed analysis shows that SPCQ can resist various security threats from telemetry interface. In addition, we also implement SPCQ on an embedded device, smart phone and laptop with a real medical database, and extensive simulation results demonstrate that our proposed SPCQ scheme is highly efficient in terms of computation and communication costs.
随着人体传感器网络的发展以及智能手机的普及，人体传感器能够实时收集不同类型的个人数据，海量个人数据的潜在价值近来引起了广泛关注。然而，敏感个人数据的隐私问题如今仍然具有挑战性。针对这些挑战，在本文中，我们聚焦于遥测接口带来的威胁，并提出一种用于外包计算的安全且保护隐私的人体传感器数据收集与查询方案，名为SPCQ。在所提出的SPCQ方案中，用户的个人信息由不同类型的人体传感器收集并转换为多维数据，每个维度被转换为数字形式并上传到云服务器，云服务器提供安全、高效且准确的数据查询服务，同时保证敏感个人信息和用户查询数据的隐私性。具体而言，基于对复合阶群的改进同态加密技术，我们针对加密数据上的多维向量提出了一种特殊的加权欧几里得距离对比算法（WEDC）。通过SPCQ方案，在云服务器中能够实现敏感个人数据的保密性、数据用户查询的隐私性以及准确的查询服务。详细分析表明SPCQ能够抵御来自遥测接口的各种安全威胁。此外，我们还在带有真实医疗数据库的嵌入式设备、智能手机和笔记本电脑上实现了SPCQ，大量仿真结果表明我们提出的SPCQ方案在计算和通信成本方面具有很高的效率。
