  The integration of permissioned blockchain such as Hyperledger fabric (HF)
and Industrial internet of Things (IIoT) has opened new opportunities for
interdependent supply chain partners to improve their performance through data
sharing and coordination. The multichannel mechanism, private data collection
and querying mechanism of HF enable private data sharing, transparency,
traceability, and verification across the supply chain. However, the existing
querying mechanism of HF needs further improvement for statistical data sharing
because the query is evaluated on the original data recorded on the ledger. As
a result, it gives rise to privacy issues such as leak of business secrets,
tracking of resources and assets, and disclose of personal information.
Therefore, we solve this problem by proposing a differentially private enhanced
permissioned blockchain for private data sharing in the context of supply chain
in IIoT which is known as (EDH-IIoT). We propose algorithms to efficiently
utilize the $\epsilon$ through the reuse of the privacy budget for the repeated
queries. Furthermore, the reuse and tracking of $\epsilon$ enable the data
owner to get ensure that $\epsilon$ does not exceed the threshold which is the
maximum privacy budget ($\epsilon_{t}$). Finally, we model two privacy attacks
namely linking attack and composition attack to evaluate and compare privacy
preservation, and the efficiency of reuse of {\epsilon} with the default
chaincode of HF and traditional differential privacy model, respectively. The
results confirm that EDH-IIoT obtains an accuracy of 97% in the shared data for
$\epsilon_{t}$ = 1, and a reduction of 35.96% in spending of $\epsilon$.
