In the future 6G communication era, the demand for spectrum resources will reach unprecedented heights, making dynamic spectrum allocation extremely crucial. Existing dynamic spectrum sharing technologies rely on secondary users querying databases for feedback. However, malicious secondary users can infer the location of primary users through multiple queries and use Bayesian theory to update probability, posing privacy and security threats. To address this, we propose a Blockchain-based anti-Malicious Queries (BMQ)scheme. This scheme combines Dynamic Spectrum Sharing (DSS) with blockchain technology, leveraging its decentralized, tamper-proof, and secure nature to protect privacy. The BMQ scheme ensures unaffected spectrum information queries while constructing a distributed sharing environment. Additionally, we designed a consensus mechanism to detect and prevent malicious queries by determining the legality of user behavior. This mechanism addresses Bayesian-based location inference attacks, enhancing the integration of blockchain and DSS. Privacy analysis and experimental evaluation demonstrate the scheme’s effectiveness and performance advantages.
The operating system kernel, as the core component of modern software systems, plays a pivotal role in ensuring the overall security and reliability of the system. Fuzzing, an efficient vulnerability detection method, has been widely applied in the field of kernel security. The extensive use of kernel fuzzing has significantly enhanced the kernel’s resilience against attacks. This paper presents a comprehensive and systematic review of recent research in the field of kernel fuzzing. It begins by exploring the unique characteristics and potential hazards of kernel vulnerabilities, emphasizing the critical role of fuzzing in detecting these vulnerabilities. The paper then provides a detailed examination of the fundamental concepts, technical architecture, and operational workflows of kernel fuzzing systems. It surveys existing kernel fuzzing methods, highlights the evolution and technological advancements in the field, and proposes a novel taxonomy of current approaches. Finally, the review summarizes the practical achievements of kernel fuzzing and explores promising future research directions. This survey offers a structured reference framework for researchers in the field and serves as a valuable resource for advancing kernel security testing technologies.
Cloud storage has emerged as a prevalent service model for data sharing in the big data era, yet it faces significant integrity threats. While data integrity auditing has been proposed to address this challenge, existing solutions suffer from either heavy certificate overheads or single-point-of-failure risks in single Key Generation Center (KGC) architectures. To overcome these limitations, we propose a multi-KGC certificateless anonymous cloud sharing scheme with threshold-based traceability. Our scheme employs the certificateless signature with multiple KGCs to eliminate certificate overhead while resisting key escrow issues. By incorporating a group-based multisignature mechanism, we prevent attackers from distinguishing actual signers among sharing participants. Furthermore, we develop an anonymous protection and authentication approach where users generate temporary identities by XORing real IDs with secret parameters distributed via Shamir’s secret sharing, enabling threshold-based identity recovery. Cloud servers verify anonymous identifiers to prevent unauthorized access. Notably, the scheme implements threshold-based traceability: a coalition of t KGCs (n KGCs in total) can collaboratively reconstruct secret parameters to reveal a user’s real identity. Theoretical analysis demonstrates the proposal’s correctness and security. Experimental evaluations confirm its practical efficiency.
Credit risk prediction is a crucial component of modern financial analytics. With the widespread use of neural networks and cloud computing, the data owner can obtain the result of credit risk prediction through a neural network model deployed in the cloud. This approach enhances the efficiency and accuracy of credit risk assessment. However, without additional security measures, adversaries may gain access to private data and model parameters. This paper presents a verifiable and privacy-preserving credit prediction model designed to protect both the data owner’s data and the model parameters in credit risk prediction. The scheme employs a functional encryption mechanism to protect the data owner’s data and also ensures model parameters’ privacy by adding fake neurons to two non-colluding cloud servers. We employ the checksum mechanism to ensure the verifiability of the neural network computations. The experimental results demonstrate that the proposed scheme achieves a secure credit risk assessment accuracy of approximately 90
The management of case-involved property is a crucial component of the judicial, prosecutorial, and public security sectors. Ensuring the security, transparency, and compliance of property management is of paramount importance. However, traditional management systems face issues such as data tampering, information leakage, and inflexible access control. Blockchain technology, with its decentralized, tamper-proof, and transparent characteristics, offers an innovative solution to address these problems. This paper analyzes the current status and challenges of traditional case-involved property management, examines the issues associated with blockchain-based property management, and summarizes the features of current access control technologies. Based on attributes and roles, a dual-layer access control model combining internal and external mechanisms is proposed, aiming to enhance the flexibility and accuracy of access control while strengthening privacy protection. This access control mechanism can effectively prevent unauthorized access, ensure data privacy, and improve the transparency and traceability of the system. Additionally, this paper explores the integration of privacy protection technologies and access control mechanisms, as well as the potential for cross-departmental collaboration and information sharing, and anticipates the prospects of future technological optimizations. The research provides theoretical support and technical references for the application of blockchain in case-involved property management, with significant practical implications.
