Privacy-preserving Data Sharing with Attribute-based Private Matching Based on Edge Computation in the Internet-of-Things

The data sharing is essential for the analytics of specific tasks in internet-of-things (IoT). The availability and the latency of data exchange affect the validity of critical and real-time IoT services. Thus, a new computing model, i.e., edge computing model, is urgently required for data sharing in IoT. However, data sharing based on edge computing model needs to address additional security issues, i.e., the privacy protection of data acquisition and transmission against honest-but-curious edge devices. Thus, this work proposes a privacy-preserving data sharing with attribute-based private matching based on edge computation in IoT. In the proposed scheme, IoT users/devices can acquire/distribute data based on attribute-based private matching on honest-but-curious edge devices without exposing attribute/policy information and exchanged data. Moreover, the proposed scheme guarantees anonymous IoT user/device authentication with the support of handover between the regions covered by two adjacent edge devices. Nonetheless, the data transmission is of secure end-to-end communications between IoT users and devices to reduce the consumption of bandwidth between IoT users/devices and edge devices. Finally, this work implements the system to evaluate the performance and provides the security analysis of the proposed security system.