Advances in AI, IoT, and 5G/6G technologies have made data increasingly valuable for industrial and academic purposes. Data trading is evolving with blockchain adoption, which offers key security features like non-repudiation and traceability. However, challenges remain in achieving advanced security properties necessary for practical blockchain-based data trading platforms. This paper introduces a Secure Data Trading (SDT) application using blockchain to address two crucial security properties: data resellability and data verification. Data resellability enables selling the same data item to multiple buyers, while data verification allows buyers to evaluate data quality before transactions. We present a theoretical framework, detail the transaction phases, and show experimental results demonstrating that our solution effectively achieves these security properties at a reasonable cost.
With the widespread use of blockchain, scenarios requiring data modification or privacy control pose challenges to traditional immutable structures. Redactable blockchains offer a potential solution, but the performance implications of various techniques remain understudied. No prior work has provided a framework to assess the performance of these systems. This paper aims to fill this gap by presenting a comparative benchmark of redaction techniques for blockchain. The analysis focuses on chameleon hash-based, mutation-based, and votingbased approaches and offers a nuanced evaluation of the efficiency, security, and scalability trade-offs involved in these methods. Our empirical findings demonstrate that chameleon hash-based techniques are superior in terms of redaction speed, particularly within permissioned blockchain environments. Conversely, voting-based techniques excel in decentralization at the expense of latency. Mutation-based techniques, despite being straightforward, induces high storage overhead exceeding baseline blockchains by several factors. Through analytical and experimental examination of key performance indicators and potential vulnerabilities, this paper provides insights for selecting the optimal approach based on application-specific requirements.
The Waku Network is a decentralized messaging infrastructure designed to provide privacy-preserving, scalable, spam-resistant, and censorship-resistant messaging services for decentralized applications. In this paper, we present the architecture and implementation details of The Waku Network, which leverages Waku protocols to form a global, shared, peer-topeer messaging infrastructure. We discuss routing and sharding mechanisms, peer discovery methods, rate limiting techniques to prevent denial-of-service (DoS), provided services, as well as incentives for network participation. The Waku Network offers a robust messaging solution suitable for various decentralized applications, with a focus on long-term sustainability.
Preserving privacy in blockchain-based systems is crucial for ensuring anonymity and confidentiality during transactions. While cryptographic solutions can address on-chain privacy concerns, their implementation on blockchains may introduce performance overhead, which remains unclear to researchers and practitioners. This paper investigates the performance impact of integrating zero-knowledge proofs (ZKPs) into the widely adopted permissioned blockchain framework called Hyperledger Fabric. The study focuses on evaluating the scalability and bottleneck aspects of blockchain platforms incorporating ZKPs. Through comprehensive experimentation and analysis, the study reveals that the integration of ZKPs compromises performance in terms of transaction rates and latency, while effectively safeguarding users’ personal information. Implementing on-chain ZKP feature would result in a performance loss of 30% to 87.5% in various experimental configurations in Hyperledger Fabric. The findings presented in this paper are informative for the design and implementation of blockchain-based systems with strict privacy requirements.
Liquidity providing to Automated Market Maker (AMM) is critical to the smooth exchange of funds on blockchain such as Ethereum. However, dealing with risks such as Impermanent Loss is challenging, which makes people reluctant to provide liquidity. In this paper, we introduce one way to mitigate Impermanent Loss to make it easy to participate in liquidity providing.
