Ieee Transactions on Parallel and Distributed Systems Eppdr: an Efficient Privacy-preserving Demand Response Scheme with Adaptive Key Evolution in Smart Grid

By Hongwei Li, Xiaodong Lin, Haomiao Yang, Xiaohui Liang, Rongxing Lu, Xuemin Shen

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Wenxiu Ding
2. Xuyang Jing
3. Meng Hao
4. Zheng Yan
5. Hongwei Li
6. Laurence Tianruo Yang
7. Guowen Xu
8. Xizhao Luo

Citations in the corpus

• A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “There still exist many reliable and privacy-preserving schemes [94-96] for aggregating consumer data.”
• Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence (2020, background): “As a branch of artificial intelligence, deep learning (DL) is widely used to solve data-driven industrial problems in realworld applications, such as smart grid, autonomous driving, and facial recognition [5], [6].”
• Towards Efficient and Privacy-Preserving Federated Deep Learning (2019, background): “Although it guarantees the privacy of individual data, the updated gradients can still reveal individual information [12] [13] [14].”
• Energy-theft detection issues for advanced metering infrastructure in smart grid (2014, unknown): “response[5].”