Context-Aware Verifiable Cloud Computing

IEEE Internet Things J.

Ding

et al. 2019

Self Cite

148

103

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.

Section: A Open Issuesmentioning

confidence: 99%

A Survey on Secure Data Analytics in Edge Computing

Liú

IEEE Internet Things J.

Ding

et al. 2019

Self Cite

148

103

“…Since its first appearance in 1991 [20], considerable attention has been devoted into verifiable computation with more and more companies and individuals to migrate their services to cloud while the service provider can be an untrusted or even malicious party who will return plausible results without performing its actual work [21], [22].…”

Section: Related Workmentioning

confidence: 99%

Verifiable Edge Computing for Indoor Positioning

Liu

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

2020

Self Cite

Edge computing has been widely adopted in many systems, thanks for its advantages to offer low latency and alleviate heavy request loads from end users. Its integration with indoor positioning is one of promising research topics. Different from a traditional positioning system where a user normally query remotely deployed positioning services provided by a Location Information Service Provider (LIS), LIS will outsource its service to an edge device, and the user can obtain the service by directly accessing the edge device in an edge computingbased system. Though the benefits from edge computing, there is still some open issues for service outsourcing. One of them is how to ensure that the outsourced service is executed honestly by the edge device. However, the current literature has not yet seriously studied this issue with a feasible solution. In this paper, we design a verification scheme to solve this open problem for indoor positioning based on edge computing. By injecting some specially designed dataset into a trained machine learning based positioning model, the functionality of outsourced model on edge devices can be verified through this dataset with regard to its prediction accuracy from outsourced model. The verification is successful only when the prediction accuracy can pass a threshold. In experiments, we provide extensive empirical evidence using state-of-the-art positioning models based on realworld datasets to prove the effectiveness of our proposed scheme and meanwhile investigate the effects caused by different factors.

“…If the source and immediate nodes producing or transferring the data are trustworthy, the data quality and trustworthiness can be ensured [44]. In addition, data provenance can be used to detect errors in data generation and processing and find out the nodes and the actions that produced the errors, which can be used in verifiable computation [57,61,62]. On the other hand, detailed provenance information allows data recovery when data is unusable to maintain system availability and ensure smooth data communications [19].…”

Section: Usages Of Data Provenancementioning

confidence: 99%

A survey on data provenance in IoT

Ding

et al. 2019

World Wide Web

Self Cite

Internet of Things (IoT), as a typical representation of cyberization, enables the interconnection of physical things and the Internet, which provides intelligent and advanced services for industrial production and human lives. However, it also brings new challenges to IoT applications due to heterogeneity, complexity and dynamic nature of IoT. Especially, it is difficult to determine the sources of specified data, which is vulnerable to inserted attacks raised by different parties during data transmission and processing. In order to solve these issues, data provenance is introduced, which records data origins and the history of data generation and processing, thus possible to track the sources and reasons of any problems. Though some related researches have been proposed, the literature still lacks a comprehensive survey on data provenance in IoT. In this paper, we first propose a number of design requirements of data provenance in IoT by analyzing the features of IoT data and applications. Then, we provide a deepinsight review on existing schemes of IoT data provenance and employ the requirements to discuss their pros and cons. Finally, we summarize a number of open issues to direct future research.