Personal Cloudlets: Implementing a User-centric Datastore with Privacy Aware Access Control for Cloud-Based Data Platforms

McCarthy, Donal; Malone, Paul; Hange, Johannes; Doyle, Kenny; Robson, Eric; Conway, Dylan; Ivanov, Stepan; Radziwonowicz, Łukasz; Kleinfeld, Robert; Michalareas, Theodoros; Kastrinogiannis, Timotheos; Stasinos, Nikos; Lampathaki, Fenareti

doi:10.1109/telerise.2015.15

Cited by 10 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, note that certain edge paradigms allow users to deploy their own personal data centers. Consequently, some works, such as the OPENi framework [85], have studied how to grant access to external users in such personal cloudlet platforms. In OPENi, the authentication component makes use of the OpenID Connect authentication layer, amongst other mechanisms.…”

Section: Identity and Authenticationmentioning

confidence: 99%

“…There are very few studies that have investigated the development of fine-grained access control mechanisms in the context of edge paradigms. One example is the OPENi framework [85], which was mentioned in the previous section. In this framework, the authorization is based on OAuth 2.0, and the owner of the cloudlet defines the access rights of every resource by creating and storing access control lists (ACL) in a NoSQL database.…”

Section: Access Control Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges

Román

López

Mambo

2018

Future Generation Computer Systems

1,070

603

View full text Add to dashboard Cite

For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.

show abstract

Section: Identity and Authenticationmentioning

confidence: 99%

Section: Access Control Systemsmentioning

confidence: 99%

Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges

Román

López

Mambo

2018

Future Generation Computer Systems

1,070

603

View full text Add to dashboard Cite

show abstract

“…Attacks' consequences could be similar to traditional cyber-attacks; however, new attacks with serious impacts might be orchestrated, such as injection of manipulated information during a collaboration session, the deployment of disguised services and searching for victims with specific vulnerabilities. Multiple security facets have been discussed, including identity and authentication (Toosi et al, 2014;Touceda et al, 2015), access control systems (McCarthy et al, 2015;Huang et al, 2014;Vassilakis et al, 2016;Dsouza et al, 2014), protocol and network security (Kreutz et al, 2015;Li and Chen, 2015;Chen et al, 2016), intrusion detection systems (Mtibaa et al, 2015;Gai et al, 2016;Shi et al, 2015) and privacy (Ravichandran et al, 2015;Zhang et al, 2015;Huang et al, 2016). This paper focuses on trust, however, these topics have been briefly referred-to when necessary.…”

Section: Security Threatsmentioning

confidence: 99%

A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

Zineddine

2020

ICS

View full text Add to dashboard Cite

Purpose Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network. Design/methodology/approach The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm. Findings The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network. Research limitations/implications Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available. Originality/value The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

show abstract

“…Another study describes a secure access control system that uses an intermediate layer between the mobile device and the cloud to control access; the intermediate layer is composed of cloudlets deployed by the cloud provider [ 7 ]. Additionally, a third system describes a framework for personal cloudlets that uses access control that is sensitive to user privacy [ 24 ]. Other approaches use proxy signatures as an access control protocol [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust

Gimeno

Mora

Marcos-Jorquera

et al. 2018

Sensors

View full text Add to dashboard Cite

Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications.

show abstract

Personal Cloudlets: Implementing a User-centric Datastore with Privacy Aware Access Control for Cloud-Based Data Platforms

Cited by 10 publications

References 1 publication

Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges

Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges

A novel trust model for fog computing using fuzzy neural networks and weighted weakest link

A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust

Contact Info

Product

Resources

About