An attribute-driven access control framework based on smart contracts for secure collaborative predictive maintenance

Abstract Predictive maintenance systems rely on data sharing across organisations, yet commercially sensitive information requires precise access control to prevent competitive disadvantage. Existing centralised mechanisms require blind trust among participants, creating significant barriers to collaborative machine learning in industrial settings. This paper extends SDDK-AC (Secure Decentralised Data and Knowledge Access Control for Predictive Machinery Maintenance), an access control mechanism that couples Attribute-Based Access Control policies with blockchain and smart contracts, by implementing contextual attributes for geolocation verification and data integrity via hash comparison. The mechanism runs on a Hyperledger Besu permissioned blockchain, integrated with Keycloak and an Access Control Proxy. This paper evaluates 30,000 policy decisions across 30 experimental rounds, each comprising 1000 transactions, using a custom-developed Python evaluation script. The results show that most SDDK-AC functions achieve throughput above 60 transactions per second with an average latency of 14 ms, incurring approximately 16% overhead relative to a centralised ABAC baseline while still meeting predictive maintenance performance requirements.