Logging mechanism for cross-organizational collaborations using Hyperledger Fabric

Organizations nowadays are largely computerized, with a mixture of internal and external services providing them with on-demand functionality. In some situations (e.g. emergency situations), cross-organizational collaboration is needed, providing external users access to internal services. Trust between partners in such a collaboration can however be an issue. Although (federated) access control policies may be in place, it is unclear which data was requested and delivered after a collaboration has finished. This may lead to disputes between participating organizations. The open-source permissioned blockchain Hyperledger Fabric is utilized to create a logging mechanism for the actions performed by the participants in such a collaboration. This paper presents the architecture needed for such a logging mechanism and provides details on its operation. A prototype was designed in order to evaluate the performance of an asynchronous logging approach. Measurements show that the proposed logging mechanism enables organizations to create a log of service interactions with limited delay imposed on the data exchange process

Trustful ad hoc cross-organizational data exchanges based on the Hyperledger Fabric framework

Organizations share data in a cross-organizational context when they have the goal to derive additional knowledge by aggregating different data sources. The collaborations considered in this article are short-lived and ad hoc, that is, they should be set up in a few minutes at most (e.g., in emergency scenarios). The data sources are located in different domains and are not publicly accessible. When a collaboration is finished, it is however unclear which exchanges happened. This could lead to possible disputes when dishonest organizations are present. The receipt of requests/responses could be falsely denied or their content could be point of discussion. In order to prevent such disputes afterwards, a logging mechanism is needed which generates a replicated irrefutable proof of which exchanges have happened during a single collaboration. Distributed database solutions can be taken from third parties to store the generated logs, but it can be difficult to find a party which is trusted by all participating organizations. Permissioned blockchains provide a solution for this as each organization can act as a consensus participant. Although the consensus mechanism of the permissioned blockchain Hyperledger Fabric (versions 1.0-1.4) is not fully decentralized, which clashes with the fundamental principle of blockchain, the framework is used in this article as an enabler to set up a distributed database, and a proposal for a logging mechanism is presented which does not require the third party to be fully trusted. A proof of concept is implemented which can be used to experiment with different data exchange setups. It makes use of generic web APIs and behaves according to a Markov chain in order to create a fully automated data exchange scenario where the participants explore their APIs dynamically. The resulting mechanism allows a data-delivering organization to detect missing logs and to take action, for example, (temporarily) suspend collaboration. Furthermore, each organization is incentivized to follow the steps of the logging mechanism as it may lose access to data of others, otherwise. The created proof of concept is scaled to 10 organizations, which autonomously exchange different data types for 10 min, and evaluation results are presented accordingly

Tree-Chain: A Fast Lightweight Consensus Algorithm for IoT Applications

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity. Most conventional blockchains rely on computationally expensive consensus algorithms, have limited throughput, and high transaction delays. In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key. Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger. Implementation results show that tree-chain is runnable on low resource devices and incurs low processing overhead, achieving near real-time transaction settlement