Blockchain-based decentralized identity (DID) typically employs identity aggregation techniques to support efficient and trustworthy identity authentication in order to meet the requirements of the high volume of service requests in Internet of Things (IoT). Due to the lack of effective mechanisms for heterogeneous DID (H-DID) aggregation, a complete aggregated identity authentication often requires multiple rounds of signature verification for different identity attributes. However, this setting brings trust and privacy issues, and one notable threat is the potential disclosure of secret identity information through the linkage of heterogeneous identity attributes when enormous IoT devices/accesses are involved. In this article, we focus on trustworthy authentication of DID and propose a novel anonymous verifiable credential-based aggregation for h-DID (AVCA-hDID). Our AVCA-hDID model supports anonymous ownership verification of DIDs through label randomization, thereby effectively safeguarding identity privacy in IoT. AVCA-hDID involves identifier aggregation and attribute aggregation for H-DIDs, ensuring both authentication efficiency and balancing trustworthiness and adoptability. We analyze the security and unlinkablility of our proposed model and further experiment evaluation demonstrates the efficiency and effectiveness of AVCA-hDID.

Along with the development of the biological industry, the system of biological assets governance has a higher-level requirement in asset identity verification due to the demands of digitization and Financial Technology (FinTech). In order to achieve broad scalability and adaptability in the governance of biological assets' identities, this work proposes a Biological Assets Decentralized Identity Management (BA-DID) system that develops a Decentralized Identity (DID)-based solution to addressing the verification issues in biological assets while considering multi-dimensional requirements. Blockchain technology is the fundamental infrastructure of the system. Authenticated certificates of the asset owners verify identity attributes. Financial Service Institutions (FSI) and governance agencies act as issuers, providing Verifiable Credentials (VC) for biological assets by using a group of attributes over a consensus. The asset owners hold VCs and can be available to other organizations as proof of the corresponding asset attributes. Our evaluations have demonstrated that the proposed approach has superior performance in biological assets verification, security, and maintenance.

Federated learning realizes distributed machine learning training by sharing the model rather than sharing the local dataset. However, the local dataset may be leaked during model training. While differential privacy techniques can mitigate privacy leakage to some extent, the noise tends to have a significant negative impact on model accuracy. To minimize the impact of noise on model accuracy and protect the privacy of the original data, we propose an Layer-wise Relevance Propagation-based Adaptive Federated Learning (LRPAFL). To ensure local data privacy, we inject adaptive noises that satisfy DP into the training sample according to the correlation between local training data features and the model. Specifically, we set a correlation boundary ct. We only inject an adaptive amount of noise when the correlation between the feature and the model is greater than or equal to ct. Furthermore, to evaluate the performance of our approach, we propose a relationship between privacy budget and accuracy. We theoretically and experimentally analyze the performance of this model. Compared with the baseline method, our method has a better performance and the proposed model reduces the impact of noise on model accuracy while protecting data. For example, compared with the state-of-the-art scheme, the accuracy of RASFL is increased by 2% when$\epsilon=1$

The recent COVID-19 pandemic has highlighted the importance of food safety and supply chain governance. In other words, we need to ensure traceability along the supply chain and support high-frequency transactions, effective data collections, etc. Thus, we posit the potential of using a lightning network, which is a decentralized traceable paradigm for achieving high-frequency transactions in blockchain-based systems. In addition, we also utilize edge computing to help facilitate data collection. However, a key challenge in securing food supplies is determining the optimal global transaction path in the lightning network while achieving efficiency and meeting the dynamic nature of food supply management. Thus, we propose a blockchain-edge scheme that utilizes our proposed dynamic programming to produce optimal solutions for selecting global transaction paths. Specifically, our scheme optimizes routing fees under existing constraints (e.g., transmission cost, computing resource consumption, and lightning network balance). The findings from our evaluations demonstrate the utility of our proposed approach in facilitating food safety management.