基于属性加密算法的可穿戴设备系统隐私保护方法研究

A CP-ABE Privacy Preserving Method for Wearable Devices

基于无线传感器网络的可穿戴健康监测设备正改变着传统的数据监测方式,其开放式的使用环境和高效便捷的数据传输网络为人们带来更加便捷的数据监测方式和海量监测数据,但是监测数据的再利用却给用户带来了担忧。文章在传统数据加密方法的基础上改善可穿戴健康监测系统的隐私保护效率,以传统的CP-ABE方案为基础,使用数据属性和用户属性对数据访问行为加以过滤,在减少密文大小、消除冗余属性、提高加解密效率等方面,进行了两点改进:1)在LSSS矩阵生成过程中只生成授权属性相关的行,剔除其他冗余属性,减少LSSS矩阵大小;2)取得只含有授权属性集的LSSS矩阵后,结合最小授权属性搜索策略寻找最小授权集合,提出一种匿名化算法与属性基加密相融合的隐私保护模型。实验证明,文章所提方案能够一定程度上抑制冗余属性参与加解密计算,对加解密计算效率有一定的提升,而且匿名化后的访问结构并不影响最终解密过程,能够有效保护用户隐私信息。

Wearable health monitoring device based on wireless sensor networksgradually changes the traditional way of medical data monitoring, which brings the more advanced and convenient health data monitoring. However, privacy has become an issue of great concern in the transmission and distribution of monitoring data. Thus in this paper, we propose a novel privacy protection model which compromises anonymous algorithm and attribute based encryption schemes on the basis of previous studies.For cutting down LSSS matrix＇s scale and exclude redundant attributes related to privacy, the rows associated with authorized attributes are firstly computed in LSSS matrix. After LSSS matrix is computed, we combine LSSS matrix with minimum authorized collection line search strategy to find minimum authorization attribute sets.Our experiment configures that the method proposed in this paper could stop redundant attributes to participate in encryption and decryption. At the same time,theefficiency of encryption and decryption are improved obviously. Privacy information is protected well without imposing any influence on final decryption.