An Optimized Algorithm for Protecting Privacy Based on Coordinates Mean Value for Cognitive Radio Networks

• Main Authors: Ling Xing, Qiang Ma, Jianping Gao, Song Chen
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Cognitive radio networks; optimized privacy protection; location-based service; stochastic gradient descent
• Online Access: https://ieeexplore.ieee.org/document/8331063/

The cooperative spectrum sensing of cognitive radio networks is quite important in the flexibility of spectrum sharing. The fusion center makes the access decision based on the feedback local sensing information from the secondary users (SUs). However, the local sensing information, which includes the SUs' geographical data, poses a threat to the privacy of those users. Security should be preserved in order to protect the SUs' privacy. In this paper, we aim to preserve the SUs' privacy and propose a location-based privacy protection strategy based on the users' mobile trajectory. The proposed privacy protection algorithm is based on the coordinates mean value, which considers the correlation of various attributes of current privacy protection methods, e.g., the k-anonymity algorithm and the generalization algorithm. The algorithm is optimized by the stochastic gradient descent method to obtain the best performance given different k-values. Simulation results show that the proposed algorithm is effective in terms of the degree of privacy protection, average anonymous time, cost, and loss.