Pragmatic evaluation of privacy preservation security models targeted towards fog-based deployments

【Author】 Belsare, Roshan Gunwantrao; Ambhore, P. B.

【Source】INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING

【影响因子】0.000

【Abstract】Fog layer sits between cloud layer and edge-layer and responsible for selection of edge-nodes to process cloud tasks. Fog devices manage routers, gateways and other scheduling components, which makes them highly vulnerable to security attacks. Attackers inject malicious packets fog-server, middleware or sensing layers which causes a wide variety of attacks. These attacks include node capturing, signal jamming, node outage, authorization, selective forwarding, data disclosure etc. To remove these attacks, wide variety of solutions are proposed by researchers, which include authorization, cryptography, error correction, firewall, broadcast authentication, selective disclosure etc. Moreover, these solutions vary with respect to privacy and security quality metrics, attack prevention capabilities and deployment quality of service (QoS). Thus, testing and deployment of these solutions is time consuming, requires additional manpower for performance validation. Hence fog deployments require larger time-to-market and are costly than their corresponding cloud deployments. In order to reduce the time for testing and validation of these resilience techniques, this text reviews various fog security and privacy preservation models and discusses their nuances, advantages, limitations and future research scopes. Furthermore it also performs a detailed performance comparison between the reviewed models, which assists in selecting best possible approach for a given application scenario. This text also recommends various fusion based approaches that can be applied to existing security and privacy models in order to further improve their performance. These approaches include hybridization, selective augmentation and Q-learning based models that assist in improving efficiency of encryption, privacy preservation, while maintaining high QoS levels.

【Keywords】Blockchain; privacy preservation; neural; deep learning; fuzzy; security; attacks

【发表时间】2022 NOV

【收录时间】2023-02-19

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