Performing effective supervision in blockchain networks while preserving public anonymity has long been a challenging issue. Existing solutions often depend on third-party institutions or dual-chain architectures to monitor and recover user identities, but these approaches pose significant risks, such as identity disclosure and increased inter-chain communication costs. To address these concerns, this paper proposes a novel supervised scheme based on Threshold Secret-Sharing (TSS) and Zero-Knowledge Proof (ZKP) on anonymous accounts on the blockchain called TSS-ZKP, while integrating regulatory authority (RA) and multiple traceability centers (TC). Compared to traditional single RA, TSS-ZKP decentralizes the regulatory authority and storage across multiple TCs, significantly reducing the risk of user identity leakage by a single RA. By TSS-ZKP, user identities are recovered through the collaborative generation of sub-secrets by the RA and TCs, without storing actual identity information. By leveraging elliptic curves and using hash functions as secret labels, TSS-ZKP achieves lightweight operations, allowing TCs to efficiently locate sub-secrets without extensive traversal. The comprehensive analysis of security and privacy demonstrated that TSS-ZKP effectively safeguards user privacy while enabling feasible supervision. In addition, comparison experimental results show that the time consumption of the TSS-ZKP is about 30% of that of the comparison scheme. Meanwhile, the simulation results highlight the practicality of the scheme, showing that TSS-ZKP significantly reduces the delays in the identity recovery process while maintaining high usability. In general, TSS-ZKP provides a safer and more feasible solution for enabling the supervision of blockchain accounts.

Instruction reordering is an essential optimization technique used in both compilers and multi-core processors to enhance parallelism and resource utilization. Although the original intent of this technique is to benefit the program, some improper reordering can significantly impact the program correctness, which we call instruction reordering vulnerability (IRV). However, existing methods detect IRV by defining CPU instruction reordering rules to schedule execution paths while neglecting compiler reordering, and thus generate false positives that require manual filtering and resulting in inefficiency. To bridge this gap, in this paper, we propose the IRV detection method, , which analyzes IRV characteristics and extracts vulnerability patterns, integrating program dependency analysis for compiler reordering and memory model constraints for CPU reordering. Specifically, we use static analysis based on specific patterns to narrow the analysis scope, and adopt log-based dynamic analysis to confirm vulnerability by checking the log constraints. We built the IRV benchmark to compare IRHunter with five state-of-the-art tools (i.e., GENMC, Nidhugg, CBMC, SHB, BiRD). IRHunter detected all 19 errors, doubling the best model checking tools' performance, with half the false positive rate of leading data race detectors. It was 10× faster on small programs and outperformed data race detectors on large programs.