In the context of large-scale data collection like in the Internet of Things, Data Confidence Fabrics are expected to play an essential role in verifying and authenticating sensor data. To this end, metadata is generated at each network node and stored on the blockchain. However, storing metadata on the blockchain introduces significant privacy risks, as it can be exploited to reveal sensitive information, such as network structures and communication paths. This paper addresses these challenges by proposing two novel schemes to protect network structures: Hostname Mapping and Hostname Encryption . Our work demonstrates that the Hostname Mapping approach effectively conceals network patterns but introduces inefficiencies due to additional table storage and computational overhead. In contrast, the Hostname Encryption method eliminates the need for additional table management, offering a more efficient and secure alternative. Despite these advancements, the timestamp field in metadata could still allow attackers to infer patterns using machine learning, highlighting the need for further research to fully secure metadata. By combining encryption with some enhancements to timestamp obfuscation, our approach lays the foundation for additional privacy protection against metadata exploitation.

The need to trust data has become a key requirement in modern distributed systems. To facilitate measurable trust and confidence in data and applications spanning heterogeneous systems, the emerging concept of a Data Confidence Fabric (DCF) offers a compelling solution. Data producers and processors provide metadata, known as annotations, recording trust insertion along the data delivery chain. Thus, it is possible to asses the trustworthiness of data before processing it. While a DCF, such as the Alvarium framework, enables this management of trust, there is a cost in terms of the overheads associated with security annotations themselves. To improve efficiency of a DCF, we therefore propose a set of techniques for making annotations more compact and to reduce the number of DCF transactions. Our work shows that transaction efficiency gains of up to 93% in our considered use cases can be achieved.