Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Zhang, Yinghui; Zhao, Jiangfan; Zheng, Dong; Deng, Kaixin; Ren, Fangyuan; Zheng, Xiaokun; Shu, Jiangang

doi:10.3390/s18082659

Cited by 41 publications

(32 citation statements)

References 51 publications

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“…Let G 1 , G 2 be a cyclic addition group on elliptic curve and a cyclic multiplication with same order q, where q is a large prime and P 0 is a generator of G 1 .ê : G 1 ×G 1 → G 2 satisfies the following properties (see also [17]):…”

Section: Preliminariesmentioning

confidence: 99%

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Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Wang

Liu

Choo

2021

IEEE Trans. Ind. Inf.

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As smart cities and nations are fast becoming a reality, so does the underpinning infrastructure such as smart grids. One particular challenge associated with smart grid implementation is the need to ensure privacy preserving multisubset data aggregation. Existing approaches generally require the collaboration of a trusted third party (TTP), which may not be practical. This also increases the threat exposure, as the attacker can now target the TTP who may be servicing several smart grid operators. Therefore, in this paper, a fault-tolerant multi-subset data aggregation scheme is proposed. Our scheme aggregates the total electricity consumption value,and obtains the number of users and the total electricity consumption in different numerical intervals, without relying on any TTP. Detailed system analysis shows that our scheme prevents the leakage of single data, as well as guarantees the efficiency when new user joins and existing user leaves. Findings from our evaluation also demonstrate that system robustness is achieved with negligible cost.

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Section: Preliminariesmentioning

confidence: 99%

“…The virtual aggregation domain was proposed in 3PDA [16], which allows users to join the data aggregation dynamically. In [17], false data injection attack resistance is achieved using the blinding factor. However, it is vulnerable to collusion attacks from control centers and fog devices, and cannot support users dynamic join and exit.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Wang

Liu

Choo

2021

IEEE Trans. Ind. Inf.

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“…However, data can be overexposed and revealed to the outside world because of this additional fog layer. The data generated by IoT devices will be computed by the nearest fog nodes, and false data injection attacks can occur when data are outsourced to the fog nodes [57]. Since fog nodes are close to IoT devices, location privacy is another issue.…”

Section: E Privacy Preserving In Fog Nodesmentioning

confidence: 99%

Access Control in Fog Computing: Challenges and Research Agenda

2020

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Fog computing is an intermediate computing layer that has emerged to address the latency issues of cloud-based Internet of things (IoT) environments. As a result, new forms of security and privacy threats are emerging. These threats are mainly due to the huge number of sensors, as well as the enormous amount of data generated in IoT environments that needs to be processed in real time. These sensors send data to the cloud through the fog computing layer, creating an additional layer of vulnerabilities. In addition, the cloud by nature is vulnerable because cloud services can be located in different geographical locations and provided by multiple service providers. Moreover, cloud services can be hybrid and public, which exposes them to risks due to their infinite number of anonymous users. Access control (AC) is one of the essential prevention measures to protect data and services in computing environments. Many AC models have been implemented by researchers from academia and industry to address the problems associated with data breaches in pervasive computing environments. However, the question of which AC model(s) should be used to prevent unauthorized access to data remains. The selection of AC models for cloud-based IoT environments is highly dependent on the application requirements and how the AC models can impact the computation overhead. In this paper, we survey the features and challenges of AC models in the fog computing environment. We also discuss the diversity of different AC models. This survey provides the reader with state-of-the-art practices in the field of fog computing AC and helps to identify the existing gaps within the field.

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“…These applications can be used to generate big data to provide new services to citizens, businesses, and public administrations to make smart decisions [3]. More in-depth understanding of IoT with its applications, challenges, and open research issues is discussed in [1][2][3][4][5][6][7]. Many benefits are provided by IoT technologies to the healthcare field, and the resulting applications can be grouped mainly in the tracking of objects and people (staff and patients); identification and authentication of persons; automatic data collection and detection [8].…”

Section: Introductionmentioning

confidence: 99%

EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

Almalki

Soufiene

2021

Wireless Communications and Mobile Computing

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Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

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Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Cited by 41 publications

References 51 publications

Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Fault-Tolerant Multisubset Aggregation Scheme for Smart Grid

Access Control in Fog Computing: Challenges and Research Agenda

EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

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