Transparent, trustworthy and privacy-preserving supply chains

Over the years, supply chains have evolved from a few regional traders to globally complex chains of trade. Consequently, supply chain management systems have become heavily dependent on digitization for the purpose of data storage and traceability of goods. However, these traceability systems suffer from issues such as scattering of information across multiple silos and susceptibility of erroneous or modified data and thus are often unable to provide reliable information about a product. Due to propriety reasons, often end-to-end traceability is not available to the general consumer. The second issue is ensuring the credibility of the collated information about a product. The digital data may not be the true representation of the physical events which raises the issues of trusting the available information. If the source of digital data is not trustworthy, the provenance or traceability of a product becomes questionable. The third issue in supply chain management is a trade-off between the provenance information and protection of this data. The information is often associated with the identity of the contributing entity to ensure trust. However, the identity association makes it difficult to protect trade secrets such as shipments, pricing, and trade frequency of traders while simultaneously ensuring the provenance/traceability to the consumers. Our work aims to address above mentioned challenges related to traceability, trustworthiness and privacy. To support traceability and provenance, a consortium blockchain based framework, ProductChain, is proposed which provides an immutable audit trail of the supply chain events pertaining to the product and its origin. The framework also presents a sharded network model to meet the scalability needs of complex supply chains. Simulation results for our Proof of Concept (PoC) implementation show that query time for retrieving end-to-end traceability is of the order of a few milliseconds even when the information is collated from multiple regional blockchains. Next, to ensure the credibility of data from the supply chain entities, it is important to have an accountability mechanism which can penalise or reward the entities for their dishonest or honest contributions, respectively. We propose the TrustChain framework, which calculates a trust score for data contributing entities to the blockchain using multiple observations. These observations include feedback from interactions among supply chain entities, inputs from third party regulators and readings from IoT sensors. The integrated reputation system with blockchain, dynamically assigns trust and reputation scores to commodities and traders using smart contracts. A PoC implementation over Hyperledger Fabric shows that TrustChain incurs minimal overheads over a baseline. For protecting trade secrets while simultaneously ensuring traceability, PrivChain is proposed. PrivChain's framework allows traders to share computation or proofs in support of provenance and traceability claims rather than sharing the data itself. The framework also proposes an integrated incentive mechanism for traders providing such proofs. A PoC implementation on Hyperledger Fabric reveals a minimal overhead of using PrivChain as the data related computations are carried off-chain. Finally, we propose TradeChain which addresses the issue of preserving the privacy of identity related information with the blockchain data and gives greater access control to the data owners, i.e. traders. This framework decouples the identities of traders by managing two ledgers: one for managing decentralised identities and another for recording supply chain events. The information from both ledgers is then collated using access tokens provided by the data owners. In this way, they can dynamically control access to the blockchain data at a granular level. A PoC implementation is developed both on Hyperledger Indy and Fabric and we demonstrate minimal overheads for the different components of TradeChain