Integrating transparency and privacy in grievance redressal through Hyperledger Fabric with multi-organization support

Grievance redressal is a vital component of academic institutions, ensuring student concerns and complaints are effectively addressed without biases. Traditional systems that encompass both manual and online solutions often encounter a wide range of challenges, such as data tampering, opacity in the redressal process, a single point of failure, and privacy concerns. This paper proposes a blockchain-based student grievance redressal system utilizing the Hyperledger Fabric blockchain framework to address these challenges. The proposed system ensures security, requisite transparency, immutability, decentralized storage, multi-organizational support, and automates significant phases of the process by effectively utilizing smart contracts. The proposed system facilitates decentralized storage for complaint registration, mitigates the single point of failure issue, and adeptly manages the privacy of sensitive data pertaining to the pursuer. It also elaborates on the design and implementation details and various key aspects. The performance of the proposed system is tested by injecting a heavy transaction load at varying transmission rates using Hyperledger Caliper. The result exhibits a 100% success rate with context to committing transactions across all test rounds, varying from 500 to 1500 transactions processed at increasing transactions per second (TPS) rates from 25 to 200 TPS, respectively. The system consistently achieved a peak throughput of 117.19 TPS at a send rate of 175 transactions/sec with zero failures, while maintaining low latency. It took only 12.80 s to successfully commit 1500 transactions at 175 TPS. The system demonstrated that the throughput maintains steady growth till 100 TPS, almost equal to the sent rate. It experienced mild performance saturation at 200 TPS but still commits all the sent transactions without any transaction failure, which shows robust performance and reliability for high-volume grievance workflows in academic environments.