The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT
Open accessAbstract
Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices' data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices' data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.
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Cites
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Cited by
All one needs to know about fog computing and related edge computing paradigms: A complete survey
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.02.009 · Cited by 1,368
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions
Ni, J., Zhang, K., Lin, X., & Shen, X. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2762345 · Cited by 659
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Security and privacy in fog computing: Challenges
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017) · IEEE Access · doi:10.1109/access.2017.2749422 · Cited by 533
Resource Management in Fog/Edge Computing: A Survey on Architectures, Infrastructure, and Algorithms
Hong, C.-H., & Varghese, B. (2020) · ACM Computing Surveys · doi:10.1145/3326066 · Cited by 461
Metadata
- Authors
- Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A.
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 3302-3312
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2591915039
- Cited by
- 478
- References
- 27
- FWCI
- 39.44
- Type
- journal article
- Language
- en
- Open access
- gold
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- Lu, R., Heung, K., Lashkari, A. H., & Ghorbani, A. A. (2017). A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT. IEEE Access. https://doi.org/10.1109/access.2017.2677520
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