Visible to the public Biblio

Filters: Author is He, Peixuan  [Clear All Filters]
2020-08-17
He, Peixuan, Xue, Kaiping, Xu, Jie, Xia, Qiudong, Liu, Jianqing, Yue, Hao.  2019.  Attribute-Based Accountable Access Control for Multimedia Content with In-Network Caching. 2019 IEEE International Conference on Multimedia and Expo (ICME). :778–783.
Nowadays, multimedia content retrieval has become the major service requirement of the Internet and the traffic of these contents has dominated the IP traffic. To reduce the duplicated traffic and improve the performance of distributing massive volumes of multimedia contents, in-network caching has been proposed recently. However, because in-network content caching can be directly utilized to respond users' requests, multimedia content retrieval is beyond content providers' control and makes it hard for them to implement access control and service accounting. In this paper, we propose an attribute-based accountable access control scheme for multimedia content distribution while making the best of in-network caching, in which content providers can be fully offline. In our scheme, the attribute-based encryption at multimedia content provider side and access policy based authentication at the edge router side jointly ensure the secure access control, which is also efficient in both space and time. Besides, secure service accounting is implemented by letting edge routers collect service credentials generated during users' request process. Through the informal security analysis, we prove the security of our scheme. Simulation results demonstrate that our scheme is efficient with acceptable overhead.
2021-12-02
Gai, Na, Xue, Kaiping, He, Peixuan, Zhu, Bin, Liu, Jianqing, He, Debiao.  2020.  An Efficient Data Aggregation Scheme with Local Differential Privacy in Smart Grid. 2020 16th International Conference on Mobility, Sensing and Networking (MSN). :73–80.
Smart grid achieves reliable, efficient and flexible grid data processing by integrating traditional power grid with information and communication technology. The control center can evaluate the supply and demand of the power grid through aggregated data of users, and then dynamically adjust the power supply, price of the power, etc. However, since the grid data collected from users may disclose the user's electricity using habits and daily activities, the privacy concern has become a critical issue. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring the trusted third party. In this paper, we propose a privacy-preserving smart grid data aggregation scheme satisfying local differential privacy (LDP) based on randomized response. Our scheme can achieve efficient and practical estimation of the statistics of power supply and demand while preserving any individual participant's privacy. The performance analysis shows that our scheme is efficient in terms of computation and communication overhead.