Differential Privacy-based Permissioned Blockchain for Private Data Sharing in Industrial IoT

TL;DR

This paper introduces a differential privacy-enhanced permissioned blockchain model using Hyperledger fabric to securely share sensitive industrial IoT data, balancing privacy with data utility.

Contribution

It integrates differential privacy into Hyperledger fabric's smart contracts to protect sensitive data during queries in supply chain scenarios.

Findings

Maintains 96.15% data accuracy with privacy guarantees.

Outperforms default chaincode in privacy preservation.

Effective in simulated IIoT supply chain environment.

Abstract

Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. The proposed work (DH-IIoT) is evaluated by simulating a permissioned blockchain using Hyperledger fabric. We compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledger's data.