Layered data aggregation with efficient privacy preservation for fog‐assisted IIoT,International Journal of Communication Systems

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Layered data aggregation with efficient privacy preservation for fog‐assisted IIoT
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-03-17 , DOI: 10.1002/dac.4381
Yalan Li _{1,

2} , Siguang Chen _{1,

3} , Chuanxin Zhao ₃ , Weifeng Lu ₂

Affiliation

The emergence of fog computing facilitates industrial Internet of Things (IIoT) to be more real‐time and efficient; in order to achieve secure and efficient data collection and applications in fog‐assisted IIoT, it usually sacrifices great computation and bandwidth resources. From the low computation and communication overheads perspective, this paper proposes a layered data aggregation scheme with efficient privacy preservation (LDA‐EPP) for fog‐assisted IIoT by integrating the Chinese remainder theorem (CRT), modified Paillier encryption, and hash chain technology. In LDA‐EPP scheme, the entire network is divided into several subareas; the fog node and cloud are responsible for local and global aggregations, respectively. Specially, the cloud is able to obtain not only the global aggregation result but also the fine‐grained aggregation results of subareas, which enables that can provide fine‐grained data services. Meanwhile, the LDA‐EPP realizes data confidentiality by the modified Paillier encryption, ensures that both outside attackers and internal semi‐trusted nodes (such as, fog node and cloud) are unable to know the privacy data of individual device, and guarantees data integrity by utilizing simply hash chain to resist tempering and polluting attacks. Moreover, the fault tolerance is also supported in our scheme; ie, even though some IIoT devices or channel links are failure, the cloud still can decrypt incomplete aggregation ciphertexts and derive expected aggregation results. Finally, the performance evaluation indicates that our proposed LDA‐EPP has less computation and communication costs.

中文翻译：

用于雾辅助IIoT的分层数据聚合和有效的隐私保护

雾计算的出现促进了工业物联网（IIoT）的实时性和效率。为了在雾辅助的IIoT中实现安全有效的数据收集和应用，通常会牺牲大量的计算和带宽资源。从低计算和通信开销的角度出发，本文通过集成中文余数定理（CRT），改进的Paillier加密和哈希链技术，为雾辅助IIoT提出了一种具有有效隐私保护（LDA-EPP）的分层数据聚合方案。在LDA-EPP方案中，整个网络分为几个子区域。雾节点和云分别负责本地和全局聚合。特别，云不仅可以获取全局聚合结果，还可以获取子区域的细粒度聚合结果，从而可以提供细粒度的数据服务。同时，LDA-EPP通过改进的Paillier加密实现数据机密性，确保外部攻击者和内部半信任节点（例如，雾节点和云）都无法知道单个设备的隐私数据，并确保数据完整性通过简单地使用哈希链来抵制攻击和污染攻击。此外，我们的方案还支持容错功能。即，即使某些IIoT设备或通道链接出现故障，云仍可以解密不完整的聚合密文并获得预期的聚合结果。最后，

更新日期：2020-03-17

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