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Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing

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Abstract

In recent years, the area of fog computing is receiving maximum focus due to the potential improvements in the cloud computing field. Fog computing is capable of resolving issues that includes location awareness, inadequate mobility support and high latency in the cloud computing environment. The internet of things (IoT) comprises of a collection of IoT equipments connected to fog nodes in order to aid the cloud service center for storing and processed a portion of data in prior. This process of storing and processing greatly minimizes the pressure in data processing with enhanced service and real time quality. However, data modification attacks and false data injection attacks introduce significant challenges over the fog nodes during the event of data processing. In this paper, Hybrid GSW (Gentry, Sahai, and Waters) and DM (Ducas and Micciancio) based fully homomorphic encryption (HGSW–DM–FHE) scheme was proposed for handling false data injection attacks under privacy preserving data aggregation in fog computing. This proposed HGSW–DM–FHE scheme is highly fault tolerant, since the process of data aggregation from other devices is not influenced even when the fog devices fails in operation. In addition, the proposed HGSW–DM–FHE scheme is determined to be efficient and secure by preventing the data injected from legal IoT devices are unaltered and protected immaterial to the honesty and maliciousness of the cloud control center and fog nodes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

    A. Amuthan & R. Sendhil

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  1. A. Amuthan
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  2. R. Sendhil
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Correspondence to R. Sendhil.

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Amuthan, A., Sendhil, R. Hybrid GSW and DM based fully homomorphic encryption scheme for handling false data injection attacks under privacy preserving data aggregation in fog computing. J Ambient Intell Human Comput 11, 5217–5231 (2020). https://doi.org/10.1007/s12652-020-01849-8

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  • DOI: https://doi.org/10.1007/s12652-020-01849-8

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