Lightweight and Privacy-Preserving ID-as-a-Service Provisioning in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) facilitates the customers to share resources ranging from storage to computing power to renting it to other users over the internet. VCC technology is combination of VANET and cloud computing. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. For security reasons, users would like to verify the integrity of their data once storing it in the VCC. In this paper, we will introduce a scheme for the integrity checking with the support of the Road Side Unit (RSU) for dynamic vehicular cloud. The hash functions and signatures are employed for integrity checking of messages which are uploaded from the vehicle to the cloud. We have analysed effectiveness of proposed scheme against typical attacks like tampering attack, node impersonation and application attack etc. Extensive simulations are conducted to verify the performance of the proposed scheme and the results are compared with existing research based on ID-based cryptographic scheme. The proposed scheme performs better in terms of computation cost and communication cost.

Lattice-based Attribute-based encryption is a well-known cryptographic mechanism that can resist quantum attacks and has the ability of fine-grained access control, and it has a wide range of application scenarios in current Internet of Thing (IoT) era. However, lack of efficiency and existing the problem of large ciphertext expansion rate are the main disadvantages impede the applications of this mechanism. Thus, we propose an efficient and practical ciphertext policy attribute-based encryption (CP-ABE) scheme from lattices in the paper. In this scheme, to make the secret key reusable, we adjust access tree and propose a basic access tree structure, which can be converted from disjunctive normal form, and combine it with a light post-quantum scheme of Kyber. In addition, the compression method and plaintext expansion method are introduced to optimize the scheme. Our CP-ABE scheme is secure against chosen plaintext attack under the hardness of module learning with errors problem. We implement our scheme and compare it with three recent related schemes in terms of security, function and communication cost. Experiments and comparisons show that our CP-ABE scheme has advantages in high encryption efficiency, small matrix dimension, small key sizes, and low ciphertext expansion rate, which has some merit in practice.

A survey on vehicular cloud computing

Pairing-based CP-ABE with constant-size ciphertexts and secret keys for cloud environment

A Review on Data Privacy using Attribute-Based Encryption

Vehicular cloud is getting significant research attention due to the technological advancements in smart vehicles. In near future, vehicles are envisioned to become part of a grid network providing cloud services, such as computing, storage, network, and application as a service. Vehicular cloud computing is an emerging area, designed to support delay-sensitive applications. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. New frameworks have been proposed to assimilate and efficiently manage this merger. In this survey paper, we present the recent advancements in vehicular cloud computing domain. The review is primarily focused on two areas. First, we discuss the frameworks designed to utilize the vehicles’ onboard resources to provide cloud services and highlight the design issues and research challenges. Secondly, we focus on a detailed study of mobility generators, network, and vehicular ad hoc network simulators, as well as the available vehicular data sets. We thus provide an overarching view of the complete domain of vehicular cloud computing and identify areas for future research directions.

Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare onboard resources are pooled by a VCC operator, e.g. a roadside unit, to serve computational tasks using the vehicle-as-a-resource framework. This paper investigates timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by vehicles). A learning-based algorithm, called DATEV (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel contextual-combinatorial multi-armed bandit learning framework. DATE-V is " contextual " because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is "combinatorial" because it replicates the received task and sends task replications to multiple vehicles to guarantee the service timeliness. When learning with multi-armed bandit, DATE-V also addresses the practical concern of delayed feedbacks caused by the task transmission/computational delay in using VCC. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.

Attribute-based encryption (ABE) for cloud computing access control is reviewed in this article. A taxonomy and comprehensive assessment criteria of ABE are first proposed. In the taxonomy, ABE schemes are assorted into key-policy ABE (KP-ABE) schemes, ciphertext-policy ABE (CP-ABE) schemes, anti-quantum ABE schemes, and generic constructions. In accordance with cryptographically functional features, CP-ABE is further divided into nine subcategories with regard to basic functionality, revocation, accountability, policy hiding, policy updating, multi-authority, hierarchy, offline computation, and outsourced computation. In addition, a systematical methodology for discussing and comparing existing ABE schemes is proposed. For KP-ABE and each type of CP-ABE, the corresponding access control scenario is presented and explained by concrete examples. Specifically, the syntax of ABE is given followed by the adversarial model and security goals. ABE schemes are discussed according to the design strategies and special features and are compared in the light of the proposed assessment criteria with respect to security and performance. Compared to related state-of-the-art survey papers, this article not only provides a broader 12 categories of ABE schemes, but also makes a more comprehensive and holistic comparison. Finally, a number of open research challenges in ABE are pointed out.

With the acceleration of mobile applications, mobile cloud computing is envisioned to be the best fit solution to make a compromise between users' and service providers' benefits. An extension of mobile cloud computing, vehicular cloud computing, provides another viable solution, by consolidating the benefits of mobile cloud computing and vehicular communications. Among several challenges in this environment, privacy, service price and provision delay are the most important. In this paper, we propose a framework to address these challenges in a vehicular cloud based on a quality-of-experience (QoE) approach, discuss the drawbacks of existing architectures, and propose and validate a new architecture. This architecture is an extension of a system [1] we proposed in previous work. QoE is obtained via other mobile nodes in the vehicular cloud, and re-formulated according to a weighted combination of the three key factors: privacy, price and delay. Privacy is defined as a function of the information revealed to the service provider. We evaluate our proposal via simulations, and based on the numerical results, we show that QoE-based service provisioning in a vehicular cloud improves upon a naïve service provision approach, as well as other approaches that address only one of the factors.

Nowadays, many organizations are adopting cloud computing for storing the large amount of organization’s important and private data. Here, it becomes important to mange appropriate access rights to these data as it is stored outside the organization’s boundary and is handled by the third party service providers. The Ciphertext Policy Attribute-based Encryption (CPABE) scheme is the most widely utilized technique that offers encrypted access control. In the existing implementations of CPABE scheme, one of the significant issues that need to be addressed is an efficient revocation mechanism. In this paper a direct and immediate user revocation approach for CPABE scheme is presented. The proposed method offers direct user revocation by maintaining the revocation list and to keep revocation list smaller the validity time is embedded in the user’s secret key. The revoked users are still able to access the previously generated ciphertexts. Hence, ciphertext update process is incorporated and a separate immediate revocation list is maintained so that the revoked users’ access is restricted. Also, in the proposed system the revocation information is embedded in ciphertext as a separate part so the update process of ciphertext is more efficient.KeywordsAttributesRevocationABECPABERevocation listValidity periodCloudAccess structure

Vehicular Cloud Computing is an emerging paradigm that integrates cloud computing technology within vehicular networks in a way to use and share available underutilized resources. The aim is to virtualize the vehicular network resources in a way to make them available services for applications within the networks. As such, the architectural design stands out as a main concern before been able to be put into practice. We address in this paper three related issues to vehicular cloud architectures namely: taxonomy, security and challenges. We first review the relevant existing taxonomies and then we propose a new one. Secondly, we address the security aspects that should be taken into consideration. Thirdly, we identify the different challenges that need to be solved. Finally, we summarize and provide a comparative analysis of the reviewed architectures.

Attribute-based encryption schemes with constant-size ciphertexts

In intelligent transportation systems, Vehicular Cloud Computing (VCC) is a new technology that can help ensure road security and transport efficiency. The study and evaluation of performances of a VCC is a topic of crucial interest in these environments. This paper presents a model of the computation resource allocation problem in VCC by considering heterogeneity and priority of service requests. We consider service requests from two classes, Primary service requests and Secondary service requests. We involve a Semi-Markov Decision Process (SMDP) to achieve the optimal policy that maximizes the performances of the VCC system taking into account the variability of resources, the income and the system cost. We utilize an iterative approach to achieve the optimal scheme that characterizes the action to be taken under each state. We validate our study by numerical results that show the effectiveness of the proposed SMDP-based scheme.

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

Vehicular Cloud Computing (VCC) denotes a shared pool of computing resources in a set of wirelessly connected vehicles, which are available for on- demand and rapid access based on the pay-as-you-go fashion. VCC consolidates the benefits of Mobile Cloud Computing (MCC) and vehicular communications. Prior to the VCC concept being widely adopted, several research issues and challenges need to be carefully addressed in such environment. Privacy, service cost and provisioning delay are identified as the most crucial challenges to be addressed. State of the art includes several proposals to address these issues however a combined solution that aims to address these requirements holistically does not exist. This paper refines our recently presented frameworks in [1][2], and extends it by building a generalized multi-objective framework, namely Auction-driven QoE Provisioning (AQoEP). A Trusted Third Party (TTP) approach has been adopted to address such challenges. Simulations results are extensively studied to proof the concept of the proposed framework. Through simulations, we proof that AQoEP in a vehicular cloud better fulfils the vehicular service requirements than any other proposed approach.