A Hybrid Deduplication for Secure and Efficient Data Outsourcing in Fog Computing

Koo, Dongyoung; Shin, Youngjoo; Yun, Jeong-Han; Hur, Junbeom

doi:10.1109/cloudcom.2016.0054

Cited by 21 publications

(4 citation statements)

References 15 publications

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“…Thus, it's a difficult task to implement privacy-preserving techniques between the Fog and IoT devices. However, it is significant because the users of IoT devices may be concerned about their data which is sensitive [77]. Different privacy preservation techniques, methods, and schemes are proposed across many scenarios, including cloud [78], wireless network [79], smart grid [80], health-care systems [81], and online social networks [82].…”

Section: Privacy Preservationmentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

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Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.

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Section: Privacy Preservationmentioning

confidence: 99%

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

et al. 2021

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“…In this context, Koo et al has presented a mixed secure paradigm that eliminates duplication by considering false fog saving and reserving scenarios [106]. For safeguarding of privacy, this work interjects a trusted third party (TPP) in the operation [107].…”

Section: Trust Middleware and Specimenmentioning

confidence: 99%

“…It can manage problematic system associations and accomplish safety setups befitting various application requirements. In their work, Wang et al suggest a plan to safeguard the identities of edge gadgets through the use of aliases and to ensure information concealment using a similar in form encryption method while transferring information from edge gadgets to the cloud [106].…”

Section: Safe Communicationmentioning

confidence: 99%

Fog Computing Architecture, Applications and Security Issues

Neware

Shrawankar

2020

International Journal of Fog Computing

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Fog computing spreads the cloud administrations and services to the edge of the system, and brings processing, communications and reserving, and storage capacity closer to edge gadgets and end-clients and, in the process, aims at enhancing versatility, low latency, transfer speed and safety and protection. This article takes an extensive and wide-ranging view of fog computing, covering several aspects. At the outset is the many-layered structural design of fog computing and its attributes. After that, chief advances like communication and inter-exchange, computing, etc. are delineated, while showing how these backup and facilitate the installations and various applications. Following that, it is shown that how, despite fog computing being a feature-rich platform, it is dogged by its susceptibility to several security, privacy, and safety concerns, which stem from the nature of its widely distributed and open architecture. Finally, some suggestions are advanced to address some of the safety challenges discussed so as to propel the further growth of fog computing.

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“…One of the most prominent and well-known technologies in this industry is machine learning. Machine learning was first established as a sub-domain of artificial intelligence by Arthur Samuel in 1959 [1,2]. Succeeding that, machine learning tactics became broadly employed in a variety of fields, and it is now recognised as one of the most important and rapidly evolving technologies, particularly for addressing issues such as future event prediction, disease diagnosis, market analysis, email filtering, intrusion detection, image and speech recognition, and so on [3].…”

Section: Introductionmentioning

confidence: 99%

Cyber Security Analysis and Evaluation for Intrusion Detection Systems

Abushark¹,

Khan²,

Alsolami³

et al. 2022

Computers, Materials &Amp; Continua

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Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching information security. Anomaly detection, software vulnerability diagnosis, phishing page identification, denial of service assaults, and malware identification are the foremost cyber-security concerns that require efficient clarifications. Practitioners have tried a variety of approaches to address the present cybersecurity obstacles and concerns. In a similar vein, the goal of this research is to assess the idealness of machine learning-based intrusion detection systems under fuzzy conditions using a Multi-Criteria Decision Making (MCDM)-based Analytical Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to Ideal-Solutions (TOPSIS). Fuzzy sets are ideal for dealing with decision-making scenarios in which experts are unsure of the best course of action. The projected work would support practitioners in identifying, prioritising, and selecting cybersecurityrelated attributes for intrusion detection systems, allowing them to design more optimal and effective intrusion detection systems.

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A Hybrid Deduplication for Secure and Efficient Data Outsourcing in Fog Computing

Cited by 21 publications

References 15 publications

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Fog Computing Architecture, Applications and Security Issues

Cyber Security Analysis and Evaluation for Intrusion Detection Systems

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