The rapid development of blockchain has spurred the emergence of programmable currency, finance, and society. However, blockchains are subject to severe privacy issues such as deanonymization, transaction linkability, and malicious contracts. Numerous studies in academia and industry have been dedicated to blockchain privacy protection. However, existing surveys tend to be limited in scope, often addressing only intra-chain privacy while neglecting cross-chain concerns and providing incomplete coverage of privacy protection methods. To facilitate interested researchers to comprehend the research field better, this paper presents a comprehensive survey about blockchain privacy protection utilizing a mapping study. We provide a detailed analysis of three types of privacy information, their corresponding privacy threats, four categories of privacy-preserving methods, and validation methods in intra-chain and cross-chain scenarios. We also summarize some typical applications of blockchain where privacy protection is crucial. Moreover, we highlight some deficiencies of the current study, and discuss challenges and future research directions in this field.

Blockchain technology improves supply chain management by ensuring the immutability of transaction records and facilitating process tracking. However, the transparency of blockchain raises significant privacy concerns, as sensitive information such as buyer and supplier qualifications, product specifications, and transaction amounts is often exposed. Compliance verification, which needs access to specific sensitive data for compliance checks, becomes challenging in blockchain-based privacy-preserving supply chains. This paper introduces ZKVeil, an innovative scheme utilizing zero-knowledge proof technology to maintain the confidentiality of sensitive information while ensuring compliance verification. Additionally, ZKVeil uses decentralized identifiers and verifiable credentials to ensure the authenticity of transaction data. A theoretical security analysis demonstrates the effectiveness of ZKVeil in safeguarding real sensitive data and ensuring compliance with regulations. To evaluate the performance of our scheme, we implement ZKVeil on a private blockchain of 100 nodes. Taking the shipbuilding supply chain transaction as an example, the experimental results demonstrate that ZKVeil incurs low gas consumption, execution time, and memory overhead.

Hyperledger Fabric has experienced widespread adoption across various domains, concurrently revealing the gradual emergence of privacy-related concerns. Trusted Execution Environment (TEE) is a secure and isolated environment for sensitive computations. The current TEE-based privacy protection method for Fabric has been proposed, but the method suffers from scalability issues, limited compatibility, and security problems. We present TrustFabric, a novel privacy protection method for Fabric leveraging TEE. TrustFabric transfers contracts involving private data to the TEE cluster for execution. Sensitive data are transmitted in encrypted form to ensure data privacy. We implement TrustFabric based on Intel SGX and Fabric. We analyze the effectiveness and anti-attack ability of TrustFabric, and evaluate the performance by experiments. The results indicate that TrustFabric can effectively protect the privacy data involved in the contract and mitigate Denial-ofService attacks, side-channel attacks, and masquerade attacks. Furthermore, TrustFabric has better performance in highly concurrent application scenarios.

The Proof of Work (PoW) consensus protocol stands as one of the most prevalent mechanisms in blockchain technology. However, its inherent proof mechanism inevitably leads to forking, making it vulnerable to security risks such as double-spending attacks, selfish mining, and whale attacks. Through research, blockchain characteristic parameters such as block generation time, block size, block propagation speed, number of nodes, and network connectivity will affect the fork rate of PoW blockchain. However, the existing studies only considered the effect of some of the parameters on the fork rate. There is no specific and detailed analysis of the effect factors of the fork rate, and there is no relevant quantitative evaluation. In this paper, we employ a quantitative evaluation model to delve into the effect of various characteristic parameters of the blockchain on the security of PoW in three distinct scenarios: 1) uniform computing power among all nodes; 2) selective non-participation of nodes in the computing power competition; and 3) collusion among honest nodes forming mining pools. Our analysis uncovers that smaller blocks, extended block generation time, fewer nodes, and higher network connectivity contribute to bolstering the security of the PoW blockchain. Moreover, a higher number of non-participating nodes correlates with a lower fork rate. Interestingly, collusion among nodes in mining pools exhibits no discernible effect on the fork rate of blockchain. Our findings are further validated through simulation results. The experimental results show that the block generation time should be maintained between 5 and 10 minutes and the block size should be maintained around 2 MB. For block propagation speed, the faster the better. With the increase in the number of nodes in the system, the network connectivity should be improved promptly. Furthermore, we provide security recommendations tailored for PoW blockchain designers, aimed at fortifying the resilience of their systems against potential threats.

With the wide application of the Raft consensus algorithm in blockchain systems, its safety has attracted more and more attention. However, although some researchers have formally verified the safety of the Raft consensus algorithm in most scenarios, there are still some safety problems with Raft consensus algorithm in some special scenarios, and cause problems now and then. For example, as a core part of the Raft consensus algorithm, the Raft leader election algorithm usually faces some safety problems in following scenarios: if the network communication between some nodes is abnormal, the leader node could be unstable or even cannot be elected, or the log entry cannot be updated, etc. In this paper, we model check the safety of the Raft leader election algorithm throughly using Spin. We use Promela language to model the Raft leader election algorithm and use Linear-time Temporal Logic (LTL) formulae to characterize three safety properties including stability, liveness, and uniqueness. The verification results show that the Raft leader election algorithm does not hold stability and liveness when some nodes are faulty and node log entries are inconsistent. For these safety problems, we give the suggestions for improving safety by analyzing counter examples.

Although data-driven image recognition software has widely emerged in various fields, they suffer from quality issues.Metamorphic testing has been successfully applied to AI software for alleviating test oracle problems.Nevertheless, metamorphic testing still relies on manual methods in most cases, which is timeconsuming.To improve test efficiency, a testing tool called MT4ImgRec is designed to automatically perform metamorphic testing for image recognition software.

High-quality Artificial intelligence (AI) software in different domains, like image recognition, has been widely emerged in our lives.They are built on machine learning models to implement intelligent features.However, the current research on image recognition software rarely discusses test questions, clear quality requirements, and verification methods.This paper presents a case study of a realistic image recognition application called Calorie Mama using manual and automation testing with a 3D decision table.The study results indicate the proposed method is feasible and effective in quality evaluation.