论文标题
在工业物联网中，私人增强的私人许可区块链用于私人数据共享
Differentially Private Enhanced Permissioned Blockchain for Private Data Sharing in Industrial IoT
论文作者
论文摘要
Hyperledger Fabric（HF）和工业互联网（IIOT）等许可区块链的集成为相互依存的供应链合作伙伴开辟了新的机会，以通过数据共享和协调来提高其性能。 HF的多通道机制，私人数据收集和查询机制可在整个供应链中启用私人数据共享，透明度，可追溯性和验证。但是，HF的现有查询机制需要进一步改进统计数据共享，因为该查询是根据分类帐中记录的原始数据评估的。结果，它引起了隐私问题，例如泄漏商业秘密，追踪资源和资产以及披露个人信息。因此，我们通过在IIOT的供应链中提出一个私人增强的权限的区块链来解决此问题，以供私人数据共享（EDH-IOIT）。我们建议算法通过对重复查询的隐私预算的重用来有效利用$ε$。此外，$ε$的重复使用和跟踪使数据所有者确保$ε$不超过最大隐私预算的阈值（$ε_{t} $）。最后，我们对两次隐私攻击进行建模，即将攻击和组成攻击联系起来，以评估和比较隐私保护，以及ε重用的效率分别与HF的默认链代码和传统的差异隐私模型。结果证实，EDH-IIOT在共享数据中以$ε_{t} $ = 1获得了97％的准确性，而支出的$ε_{t} $ = 1，$ε$的准确度减少了35.96％。
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 $ε$ through the reuse of the privacy budget for the repeated queries. Furthermore, the reuse and tracking of $ε$ enable the data owner to get ensure that $ε$ does not exceed the threshold which is the maximum privacy budget ($ε_{t}$). Finally, we model two privacy attacks namely linking attack and composition attack to evaluate and compare privacy preservation, and the efficiency of reuse of ε 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 $ε_{t}$ = 1, and a reduction of 35.96% in spending of $ε$.
