Fenghua Tong

With the advent of intelligent technologies, miscellaneous data containing sensitive information are explosively generated and shared. Compressive sensing methods are naturally suitable for such scenarios due to their joint compression and encryption capabilities. However, data users of most existing compressive sensing methods need to reconstruct original images before use, which brings two disadvantages. First, indiscriminately requiring every data user to reconstruct images without considering their exact requirements is neither advisable nor efficient. Second, allowing all data users to reconstruct original images may cause private or confidential information exposure. To address these issues, in this paper, a novel image sharing method is proposed, which realizes efficient multilevel privacy preservation. Specifically, data owners compress the original images with designed measurement matrices through the proposed Tℓ₁-B2DLDA algorithm, which outputs dimension-reduced data with the ability to simultaneously support the subsequent classification tasks for level I data users and reconstruction tasks for level II data users. Therefore, low level data users could achieve their goals without obtaining any private or confidential information in the original images. Experiments are conducted to verify the feasibility, performance and robustness of the proposed method. Furthermore, the security of the proposed method is analyzed both theoretically and practically. The source code of the proposed method is publicly available at https://github.com/xchuxiao23/TL1-B2DLDA.

Aiming to address the distributed consensus issues for Lipschitz nonlinear multiagent systems suffering from DoS attacks, this work develops a novel distributed event-triggered secure control (DETSC) schemes that achieve leader-follower consensus with fully distributed, strict Zeno-free manners. First, a distributed secure control scheme is proposed. A novel adaptive couple parameter, two kinds of state observers, and the distributed event-triggered mechanism working in tandem with the control scheme are further raised. For the elimination of global information reliance, the adaptive couple parameter with exponential term is designed, which helps in classifying Lyapunov function behavior separately in attack and normal intervals. Zeno behavior is excluded strictly by designing the upper limit and decay rate of the event-triggered function (ETF). By constructing a Lyapunov function incorporating both the exponential term and decay rate of the ETF, the time blocking caused by DoS attacks in stability analysis is overcome. Zeno-freeness analysis gives the strict positive minimum inter-event interval. Finally, the capability of the proposed DETSC scheme is tried out through the numerical simulations on a group of single-joint robotic arms.

The advancement of cloud computing and the Internet of Things (IoT) has driven progress in image-based searchable encryption technology, which meets the escalating security demands of outsourced multimedia data in IoT scenarios. However, existing encrypted image-retrieval schemes still face critical challenges, such as the lack of cross-domain support, the low retrieval efficiency and accuracy, and the absence of reliable verifiability. To address these issues, this article proposes a verifiable and privacy-preserving cross-domain image-retrieval (VPCDIR) scheme in IoT. In our scheme, the reencryption and key transformation technologies are implemented to achieve the availability of cross-domain image retrieval, and the learning with errors (LWE)-based enhanced secure k-nearest neighbor (kNN) algorithm is used to preserve the privacy of image features. Furthermore, the hybrid index mechanism that combines clustering and locality-sensitive hashing (LSH) is designed to enhance retrieval efficiency and accuracy, and the Merkle hash tree (MHT) with the short signature realizes reliable authenticity verification of the retrieval result. Finally, formal security analysis confirms the security of our scheme. Extensive experiments on the real dataset demonstrate the efficiency and practicability of VPCDIR for cross-domain image retrieval in IoT.