J
Jinbo Xiong
Researcher at Fujian Normal University
Publications - 81
Citations - 1823
Jinbo Xiong is an academic researcher from Fujian Normal University. The author has contributed to research in topics: Encryption & Information privacy. The author has an hindex of 19, co-authored 80 publications receiving 1140 citations. Previous affiliations of Jinbo Xiong include University of North Texas & Chinese Academy of Sciences.
Papers
More filters
Journal ArticleDOI
Enhancing Privacy and Availability for Data Clustering in Intelligent Electrical Service of IoT
TL;DR: A privacy and availability data clustering (PADC) scheme based on a differential privacy algorithm and differential privacy, which enhances the selection of the initial center points and the distance calculation method from other points to center point, and attempts to reduce the outlier effect through detecting outliers during the clustering process.
Journal ArticleDOI
A Personalized Privacy Protection Framework for Mobile Crowdsensing in IIoT
TL;DR: A personalized privacy protection (PERIO) framework based on game theory and data encryption, which is then combined with game theory to construct a rational uploading strategy and a privacy-preserving data aggregation scheme to ensure data confidentiality, integrity, and real-timeness.
Journal ArticleDOI
Privacy and Security Issues in Deep Learning: A Survey
TL;DR: This paper briefly introduces the four types of attacks and privacy-preserving techniques in DL, and summarizes the attack and defense methods associated with DL privacy and security in recent years.
Journal ArticleDOI
Secure, efficient and revocable multi-authority access control system in cloud storage
TL;DR: The proposed MAACS (Multi-Authority Access Control System), a novel multi-authority attribute-based data access control system for cloud storage, is proposed and an efficient attribute-level user revocation approach with less computation cost is designed.
Journal ArticleDOI
Edge-Assisted Privacy-Preserving Raw Data Sharing Framework for Connected Autonomous Vehicles
TL;DR: This work uses the additive secret sharing technique to encrypt raw data into two ciphertexts and construct two classes of secure functions, which are then used to implement a privacy-preserving convolutional neural network (P-CNN).