Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/dac.4381 | INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS |
Keywords | DocType | Volume |
data aggregation, fog computing, industrial Internet of Things (IIoT), Paillier encryption, privacy preservation | Journal | 33 |
Issue | ISSN | Citations |
9 | 1074-5351 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yalan Li | 1 | 1 | 0.36 |
| Siguang Chen | 2 | 63 | 12.91 |
| Chuanxin Zhao | 3 | 15 | 4.40 |
| Weifeng Lu | 4 | 1 | 0.36 |