An efficient access control scheme with outsourcing capability and attribute update for fog computing

Zhang, Peng; Chen, Zehong; Liu, Joseph K.; Liang, Kaitai; Liu, Hongwei

doi:10.1016/j.future.2016.12.015

Cited by 142 publications

(117 citation statements)

References 11 publications

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“…Due to limited resources of end devices, the schemes bring huge computing cost to user devices with the increase of attributes. By taking advantage of fog node's computing power, Zhang et al [84] proposed an improved CP-ABE-based access control scheme, which ensures data confidentiality. This work offloads the generation of access control structure in encryption phase and bilinear pairing operations in decryption phase to fog nodes to reduce the computational burden of user equipment.…”

Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

131

103

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Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications such as environmental monitoring, smart navigation and smart healthcare come with new requirements such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.

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Section: Secure Data Storage In Edge Computingmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

Yan

Ding

et al. 2019

IEEE Internet Things J.

131

103

View full text Add to dashboard Cite

show abstract

“…Recently, the application of IoT in the industrial domain is gaining momentum. In this direction, the operation of complex industrial physical machinery is integrated with networked sensors and software applications for enabling "Industrial Internet" based on IoTs [20] [21]. Furthermore, the Industrial Internet Consortium has been founded in March 2014 by AT&T, CISCO, General Electric, Intel, and IBM.…”

Section: Industrial Internet Of Thingsmentioning

confidence: 99%

An Efficient Indexing Model for the Fog Layer of Industrial Internet of Things

Miao

Liu

et al. 2018

IEEE Trans. Ind. Inf.

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Given the recent proliferation in the number of smart devices connected to the Internet, the era of Internet of Things (IoT) is challenged with massive amounts of data generation. Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoTs. However, efficient discovery of services is one of the prevailing issues in the fog nodes of industrial IoTs which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel efficient multilevel index model based on equivalence relation, named the DM-index model, for service maintenance and retrieval in the fog layer of industrial IoTs to eliminate redundancy, narrow the search space, reduce both the traversed number of services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.

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“…In order to reduce the computational overheads, Liu et al [21] proposed an outsourced healthcare record access control system by moving the encryption computation offline and keeping online computation task very low. Yeh et al [22] proposed a decryption outsourcing framework for health information access control in the cloud by utilizing CSP to check whether the attributes satisfy the access policy in ciphertext, which induces the outsourced encryption and decryption scheme introduced by Zhang et al [23]. Intelligent healthcare, which is one of the intelligent services in the smart city, contains various health-related applications in MHSN, such as home care and emergency alarm [24].…”

Section: Related Workmentioning

confidence: 99%

“…The sharing data in our scheme is encrypted with CP-ABE and IBBE techniques, which are secure against chosen plaintext attack since the DBDH assumption holds [23,28]. We analyze the security properties of our scheme as follows [29].…”

Section: Security Analysismentioning

confidence: 99%

Secure and Privacy-Preserving Data Sharing and Collaboration in Mobile Healthcare Social Networks of Smart Cities

Huang

Wang

Yang

2017

Security and Communication Networks

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Mobile healthcare social networks (MHSN) integrated with connected medical sensors and cloud-based health data storage provide preventive and curative health services in smart cities. The fusion of social data together with real-time health data facilitates a novel paradigm of healthcare big data analysis. However, the collaboration of healthcare and social network service providers may pose a series of security and privacy issues. In this paper, we propose a secure health and social data sharing and collaboration scheme in MHSN. To preserve the data privacy, we realize secure and fine-grained health data and social data sharing with attribute-based encryption and identity-based broadcast encryption techniques, respectively, which allows patients to share their private personal data securely. In order to achieve enhanced data collaboration, we allow the healthcare analyzers to access both the reencrypted health data and the social data with authorization from the data owner based on proxy reencryption. Specifically, most of the health data encryption and decryption computations are outsourced from resource-constrained mobile devices to a health cloud, and the decryption of the healthcare analyzer incurs a low cost. The security and performance analysis results show the security and efficiency of our scheme.

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An efficient access control scheme with outsourcing capability and attribute update for fog computing

Cited by 142 publications

References 11 publications

A Survey on Secure Data Analytics in Edge Computing

A Survey on Secure Data Analytics in Edge Computing

An Efficient Indexing Model for the Fog Layer of Industrial Internet of Things

Secure and Privacy-Preserving Data Sharing and Collaboration in Mobile Healthcare Social Networks of Smart Cities

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