Mobile Cloud Data Research Articles

Conjunctive and Disjunctive Keyword Search over Encrypted Mobile Cloud Data in Public Key System

The searchable encryption scheme can perform keywords search operation directly over encrypted data without decryption, which is crucial to cloud storage, and has attracted a lot of attention in these years. However, it is still an open problem to develop an efficient public key encryption scheme supporting conjunctive and a disjunctive keyword search simultaneously. To achieve this goal, we introduce a keyword conversion method that can transform the query and index keywords into a vector space model. Through applying a vector space model to a predicate encryption scheme supporting inner product, we propose a novel public key encryption scheme with conjunctive and disjunctive keyword search. The experiment result demonstrates that our scheme is more efficient in both time and space as well as more suitable for the mobile cloud compared with the state-of-art schemes.

Cloud computing and big data: Technologies and applications

Cloud computing and big data: Technologies and applications

Secure data classification using superior naive classifier in agent based mobile cloud computing

The explosive growth of cloud computing had resulted in the growth of fields such as ubiquitous computing, mobile cloud computing, Big Data Analytics and Cyber Physical Systems etc., Mobile Cloud Computing (MCC) is the integration of Mobile computing and Cloud computing and has gained tremendous popularity in recent years. In MCC, mobile users access the cloud services with the mobile device. Generally, the users of mobile cloud can select their services from the broker using an agent. There is an agreement between the users and service provider called SLA. Data are of varying types and they require different security levels based on their requirement. Classification of Mobile user's data is a time-consuming process. A superior naive classifier for secure data classification in agent based mobile cloud computing is proposed in this paper. The agent classifies the user's data into three different groups namely, low, medium, high. Then, the data are encrypted using Advanced Encryption Standards (AES). Encrypted data is transferred to the broker for selecting for suitable service agent.But the broker has many to one relationship with service agents. Then the data is sent to the cloud service provider for storage. The stored data is then homomorphically encrypted which enables the cloud provider and cloud user to process the data without the need for decryption. Performance analysis demonstrates that our proposed method reduces classification time and provides better security and integrity on mobile cloud data.

Application-oriented offloading in heterogeneous networks for mobile cloud computing

ABSTRACTNowadays Internet applications have become more complicated that mobile device needs more computing resources for shorter execution time but it is restricted to limited battery capacity. Mobile cloud computing (MCC) is emerged to tackle the finite resource problem of mobile device. MCC offloads the tasks and jobs of mobile devices to cloud and fog environments by using offloading scheme. It is vital to MCC that which task should be offloaded and how to offload efficiently. In the paper, we formulate the offloading problem between mobile device and cloud data center and propose two algorithms based on application-oriented for minimum execution time, i.e. the Minimum Offloading Time for Mobile device (MOTM) algorithm and the Minimum Execution Time for Cloud data center (METC) algorithm. The MOTM algorithm minimizes offloading time by selecting appropriate offloading links based on application categories. The METC algorithm minimizes execution time in cloud data center by selecting virtual and physical machines with corresponding resource requirements of applications. Simulation results show that the proposed mechanism not only minimizes total execution time for mobile devices but also decreases their energy consumption.

MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing

Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.

CloudFile: A cloud data access control system based on mobile social trust

Personal data management at cloud with mobile devices confronts a number of practical challenges such as security, efficiency and usability. Many existing solutions suffer from high computation and communication costs, thus impractical to be deployed in mobile domains. Notably, high popularity of mobile social networking provides a good platform to assess social trust in a pervasive way, which can be applied to automatically manage personal data access at the cloud with sound performance and effectiveness. This paper presents the design and implementation of a personal cloud data access control system named CloudFile based on trust evaluation in mobile social networking. The CloudFile uses a Key Policy–Attribute Based Encryption (KP-ABE) scheme to control personal data access based on social trust in order to guarantee the safety of mobile cloud data storage. System performance tests show that the CloudFile is flexible for controlling data access, secure for personal data storage, and efficient to be deployed by mobile devices. Particularly, we investigate user acceptance on the CloudFile system. Based on user feedback, we conclude that the CloudFile is accepted with regard to perceived ease of use, practicality, user interface, playfulness and intuitive attitude.

Real people, virtual places, and the spaces in between

A sharp distinction between the virtual world and the real world continues but has become less tenable as technology has progressed. This review paper traces some early and recent views on this distinction, and highlights three: cyberscapes, cyberplaces, and code/space. Mobile applications, ubiquitous computing, and cloud data storage serve to blend the real and the virtual in a user-generated cyberspace. Within that space, knowledge production takes place in virtual spaces, as do less benign activities, such as surveillance and cybercrime. Policymakers must react to an increasingly complex environment of spaces in between real and virtual worlds.

A Secure Multi-Keyword Ranked Search by Using Conditional Random Field and Blind Storage over Encrypted Mobile Cloud Data

A Secure Multi-Keyword Ranked Search by Using Conditional Random Field and Blind Storage over Encrypted Mobile Cloud Data

Engineering searchable encryption of mobile cloud networks: when QoE meets QoP

Mobile cloud computing can effectively address the resource limitations of mobile devices, and is therefore essential to enable extensive resource consuming mobile computing and communication applications. Of all the mobile cloud computing applications, data outsourcing, such as iCloud, is fundamental, which outsources a mobile user’s data to external cloud servers and accordingly provides a scalable and “always on” approach for public data access. With the security and privacy issues related to outsourced data becoming a rising concern, encryption on outsourced data is often necessary. Although encryption increases the quality of protection (QoP) of data outsourcing, it significantly reduces data usability and thus harms the mobile user’s quality of experience (QoE). How to strike a balance between QoP and QoE is therefore an important yet challenging task. In this article we focus on the fundamental problem of QoP and QoE provisioning in searchable encryption of data outsourcing. We develop a fine-grained data search scheme and discuss its implementation on encrypted mobile cloud data, which is an effective balance between QoE and QoP in mobile cloud data outsourcing.

Enabling Efficient Multi-Keyword Ranked Search Over Encrypted Mobile Cloud Data Through Blind Storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.

Secure Framework in Data Processing for Mobile Cloud Computing

Generally Mobile Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, such a service is also relinquishing users ‘physical possession of their outsourced data, which inevitably poses new security risks towards the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a new secure framework. In addition to providing traditional computation services, mobile cloud also enhances the operation of traditional ad hoc network by treating mobile devices as service nodes, e.g., sensing services. The mobile services or sensed information, such as location coordinates, health related information, should be processed and stored in a secure fashion to protect user’s privacy in the cloud. In this paper, we present a new mobile cloud data processing framework through trust management and private data isolation. Finally, an implementation pilot for improving teenagers’ driving safety, which is called FocusDrive, is presented to demonstrate the solution.

Robust Locally Weighted Regression For Ground Surface Extraction In Mobile Laser Scanning 3D Data

Abstract. A new robust way for ground surface extraction from mobile laser scanning 3D point cloud data is proposed in this paper. Fitting polynomials along 2D/3D points is one of the well-known methods for filtering ground points, but it is evident that unorganized point clouds consist of multiple complex structures by nature so it is not suitable for fitting a parametric global model. The aim of this research is to develop and implement an algorithm to classify ground and non-ground points based on statistically robust locally weighted regression which fits a regression surface (line in 2D) by fitting without any predefined global functional relation among the variables of interest. Afterwards, the z (elevation)-values are robustly down weighted based on the residuals for the fitted points. The new set of down weighted z-values along with x (or y) values are used to get a new fit of the (lower) surface (line). The process of fitting and down-weighting continues until the difference between two consecutive fits is insignificant. Then the final fit represents the ground level of the given point cloud and the ground surface points can be extracted. The performance of the new method has been demonstrated through vehicle based mobile laser scanning 3D point cloud data from urban areas which include different problematic objects such as short walls, large buildings, electric poles, sign posts and cars. The method has potential in areas like building/construction footprint determination, 3D city modelling, corridor mapping and asset management.

SECURE FRAMEWORK IN DATA PROCESSING FOR MOBILE CLOUD COMPUTING

Generally Mobile Cloud storage enables users to remotely store their data and enjoy the on-demand high quality cloud applications without the burden of local hardware and software management. Though the benefits are clear, such a service is also relinquishing users ‘physical possession of their outsourced data, which inevitably poses new security risks towards the correctness of the data in cloud. In order to address this new problem and further achieve a secure and dependable cloud storage service, we propose in this paper a new secure framework. In addition to providing traditional computation services, mobile cloud also enhances the operation of traditional ad hoc network by treating mobile devices as service nodes, e.g., sensing services. The mobile services or sensed information, such as location coordinates, health related information, should be processed and stored in a secure fashion to protect user’s privacy in the cloud. In this paper, we present a new mobile cloud data processing framework through trust management and private data isolation. Finally, an implementation pilot for improving teenagers’ driving safety, which is called FocusDrive, is presented to demonstrate the solution.

Research on Mobile Cloud Computing

Mobile cloud computing introduces the cloud computing technology to the mobile Internet. It can greatly promote the development and application of cloud computing, and can promote the development and application of the Internet technology to a higher level. Mobile cloud computing system consists of mobile devices, wireless networks and cloud data centers. It combines cloud computing technology and mobile Internet technology. With the combination of cloud computing and mobile Internet, mobile cloud computing causes some new research issues belonging to the field of mobile cloud computing. This paper introduces some research directions of mobile cloud computing such as application partition, computation offloading, resource management and context-aware services as well as future research trends in brief.

Cyber Foraging: Bridging Mobile and Cloud Computing

This lecture provides an introduction to cyber foraging, a topic that lies at the intersection of mobile and cloud computing. Cyber foraging dynamically augments the computing resources of mobile computers by opportunistically exploiting fixed computing infrastructure in the surrounding environment. In a cyber foraging system, applications functionality is dynamically partitioned between the mobile computer and infrastructure servers that store data and execute computation on behalf of mobile users. The location of application functionality changes in response to user mobility, platform characteristics, and variation in resources such as network bandwidth and CPU load. Cyber foraging also introduces a new, surrogate computing tier that lies between mobile users and cloud data centers. Surrogates are wired, infrastructure servers that offer much greater computing resources than those offered by small, battery-powered mobile devices. Surrogates are geographically distributed to be as cl se as possible to mobile computers so that they can provide substantially better response time to network requests than that provided by servers in cloud data centers. For instance, surrogates may be co-located with wireless hotspots in coffee shops, airport lounges, and other public locations. This lecture first describes how cyber foraging systems dynamically partition data and computation. It shows how dynamic partitioning can often yield better performance, energy efficiency, and application quality than static thin-client or thick-client approaches for dividing functionality between cloud and mobile computers. The lecture then describes the design of the surrogate computing tier. It shows how strong isolation can enable third-party computers to host computation and store data on behalf of nearby mobile devices. It then describes how surrogates can provide reasonable security and privacy guarantees to the mobile computers that use them. The lecture concludes with a discussion of d ta staging, in which surrogates temporarily store data in transit between cloud servers and mobile computers in order to improve transfer bandwidth and energy efficiency. Table of Contents: Introduction / Partitioning / Management / Security and Privacy / Data Staging / Challenges and Opportunities