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

Abstract

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.

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

“…It makes it with the help of a PPDP method of choice, which is independent of the actual mashup strategy. In contrast, privacy-preserving data partitioning solutions used in fog computing environments are based on simple noise addition [31].…”

“…The larger the number of sensitive attributes in an access-controlled dataset, the greater the loss of analytical utility if exposing only publicly available data attributes. Data partitioning has been proposed as a method for privacy preservation in distributed environments [31]. It is based on a simple PPDP strategy of creating noisy data along with actual data and uploading it to multiple nodes.…”

Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures

“…This paper is organized as follows: Section 2 discusses related research about the cyber security [7][8][9][10], automation [11][12][13], and asset management [14][15][16][17] for IIoT and OT systems. Section 3 proposes the modeling method and data structure for IIoT systems, and Section 4 designs the asset configuration management method.…”

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

“…Due to the rapid adoption of IoT technologies, they are becoming an attractive target for cyber criminals who take advantage of lack of security functions and abilities to compromise IoT devices. Therefore, a plethora of research is emerging to elucidate the security attacks and different security and trust management mechanisms involved in IoT applications [26,27].…”