Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.

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Study of data security and privacy preserving solutions in cloud computing

2015 International Conference on Green Computing and Internet of Things (ICGCIoT) ◽

10.1109/icgciot.2015.7380627 ◽

2015 ◽

Cited By ~ 4

Author(s):

Siddharth Dutt Choubey ◽

Mohit Kumar Namdeo

Keyword(s):

Cloud Computing ◽

Data Security ◽

Privacy Preserving ◽

Security And Privacy

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Preserving Health Care Data Security and Privacy Using Carmichael's Theorem-Based Homomorphic Encryption and Modified Enhanced Homomorphic Encryption Schemes in Edge Computing Systems

Big Data ◽

10.1089/big.2021.0012 ◽

2021 ◽

Author(s):

K. Anitha Kumari ◽

Avinash Sharma ◽

Chinmay Chakraborty ◽

M. Ananyaa

Keyword(s):

Health Care ◽

Data Security ◽

Homomorphic Encryption ◽

Edge Computing ◽

Security And Privacy ◽

Computing Systems ◽

Health Care Data ◽

Encryption Schemes

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Big Data Security and Privacy: A Review on Issues, Challenges and Privacy Preserving Methods

International Journal of Computer Applications ◽

10.5120/ijca2017915713 ◽

2017 ◽

Vol 177 (4) ◽

pp. 23-28

Author(s):

Anupama Jha ◽

Meenu Dave ◽

Supriya Madan

Keyword(s):

Big Data ◽

Data Security ◽

Privacy Preserving ◽

Security And Privacy

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TrustChain: A Privacy Preserving Blockchain with Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2019/2014697 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 10

Author(s):

Upul Jayasinghe ◽

Gyu Myoung Lee ◽

Áine MacDermott ◽

Woo Seop Rhee

Keyword(s):

Real Time ◽

Personal Data ◽

Privacy Preserving ◽

Edge Computing ◽

Security And Privacy ◽

Privacy Concerns ◽

Massive Scale ◽

Potential Alternative ◽

Iot Devices ◽

Scarcity Of Resources

Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.

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Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

Security and Communication Networks ◽

10.1155/2020/8872586 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zhanyang Xu ◽

Wentao Liu ◽

Jingwang Huang ◽

Chenyi Yang ◽

Jiawei Lu ◽

...

Keyword(s):

Artificial Intelligence ◽

Privacy Preservation ◽

Edge Computing ◽

Learning Ability ◽

Security And Privacy ◽

Model Parameters ◽

Paradigm Shifts ◽

Privacy Leakage ◽

Computing Paradigm ◽

Comprehensive Survey

With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by transferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high network latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes (ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However, compared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for restricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful learning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain extent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the contribution of AI to the IoT security in EC is presented. First, the research status and some basic definitions are introduced. Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC are discussed.

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Dependable and Secure Voting Mechanism in Edge Computing

Future Internet ◽

10.3390/fi11120262 ◽

2019 ◽

Vol 11 (12) ◽

pp. 262

Author(s):

Pedro A.R.S. Costa ◽

Marko Beko

Keyword(s):

Middle Layer ◽

Edge Computing ◽

Cloud Environment ◽

Computing Paradigm ◽

Edge Network ◽

And Storage ◽

The Internet Of Things ◽

Multi Cloud ◽

Voting Mechanism ◽

Open Issues

Edge computing is a distributed computing paradigm that encompasses data computing and storage and is performed close to the user, efficiently guaranteeing faster response time. This paradigm plays a pivotal role in the world of the Internet of Things (IoT). Moreover, the concept of the distributed edge cloud raises several interesting open issues, e.g., failure recovery and security. In this paper, we propose a system composed of edge nodes and multiple cloud instances, as well as a voting mechanism. The multi-cloud environment aims to perform centralized computations, and edge nodes behave as a middle layer between edge devices and the cloud. Moreover, we present a voting mechanism that leverages the edge network to validate the performed computation that occurred in the centralized environment.

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