Remote Cloud Servers Research Articles

Toward Assured Data Deletion in Cloud Storage

Outsourcing data to remote cloud servers can significantly reduce the managing overhead and storage burden for individuals and enterprises. When the data stored in cloud are no longer needed, assured data deletion is a fundamental requirement to ensure that the sensitive data on the cloud can be safely deleted. Salient features of cloud storage such as virtualization, multi-tenancy, high availability and the platform complexity pose various challenges to the assured data deletion, which need to be addressed. In this article, we investigate the system model, desirable security properties as well as the potential solutions to the issues of assured deletion. We present the techniques used to achieve verifiable deletion and propose several methods which satisfy the timeliness, fine granularity and the verification of deletion at the same time. We consider various scenarios such as assured deletion with data transfer, fine-grained access control and data storage with multiple copies. Implementation results demonstrate the efficiency of the proposed protocols.

Method for Evaluating Security of Cloud IT-Components based on Estandards Criteria

The analysis of well-known methods for ensuring IT-security is presented, methods for evaluating security of IT-components and Cloud services in general are considered.
 An attempt to analyze cloud services not from a commercial position of a popular marketing product, but from a position of system analysis is made. The previously introduced procedure for IT-components evaluation is not stable, since the end user has not a 100% guarantee of access to all IT-components, and even more so to the remote and uncontrolled Cloud service. A number of reviews point at increased efforts to create a secure network architecture and ability to continuously monitor deviations from established business goals. In contrast to the Zero Trust and Zero Trust eXtended models, according to which additional security functions are superimposed on existing IT-components, it is proposed to consider the set of IT-components as a new entity – an Information Processing System. This will allow to move to formal processes for assessing the degree of compliance with the criteria of standards for both existing and prospective IT-components while ensuring security of Cloud services.
 A new method for evaluation which is based on the previously developed hybrid methodology using formal procedures based on two systems of criteria - assessment of the degree of compliance of Management systems (based on ISO/IEC 27001 series) and assessment of functional safety requirements (based on IEC 61508 series and ISO/IEC 15408 series) is proposed. This method provides reproducible and objective assessments of security risks of Cloud-based IT‑components that can be presented to an independent group of evaluators for verification. The results obtained can be applied in the independent assessment, including critical information infrastructure objects.

An efficient data integrity auditing protocol for cloud computing

Cloud data storage has brought a revolution in Information Technology (IT) sector with its inherent benefits like ease of use, cost efficiency, location independence etc. By outsourcing data on cloud servers, users can reduce the hardware expenditures and can simplify the data management process. A Cloud Service Provider (CSP) is responsible for offering secure data storage services. However, using these services in a secured manner and ensuring data integrity in these remote cloud servers remains to be an issue for the clients. In this paper, we propose an efficient approach for data integrity auditing in cloud computing. The objective of proposed protocol is to minimize the computational complexity of client during system setup phase of the auditing protocol. Based on the properties of bilinear pairings, the proposed protocol is publicly verifiable and supports dynamic operations on data. The security of proposed protocol relies on the stability of Computational Diffie Hellman Problem (CDHP) in a Random Oracle Model (ROM). Finally, the performance of proposed protocol has been evaluated by implementing a prototype of the protocol and conducting experiments. The results obtained have been compared with state-of-the-art protocols and demonstrates the high efficiency and adaptability of proposed protocol by clients with limited resources.

RETRACTED ARTICLE: Energy aware smartphone tasks offloading to the cloud using gray wolf optimization

In recent era, the task offloading to cloud servers from the smartphones is a promising strategy in improving the smartphones capability and its battery lifetime. The effectiveness of task offloading is found by considering the communication cost and energy consumption, which is considered as crucial factors. These two factors enables the smartphone devices to incur proper decisions whether to perform task offloading or not. In this paper, a heterogeneous framework is designed to improve the energy efficiency of mobile phones by considering various system parameters like local cloudlets, remote cloud servers, task and non-task offloading smartphone and radio access networks. The task offloading framework uses a meta-heuristic algorithm namely Gray Wolf Optimization (GWO) to schedule the task by optimizing the system parameters and this enables to make optimal decision on task offloading. The GWO does the scheduling of tasks in optimal way to enable restriction free communication between the mobile device and cloud server. This reduces the consumption of energy in mobile devices. The GWO task offloading framework is simulated using CloudSim simulation tool and the results are compared in terms of various parameters. The result shows that the GWO task offloading framework is efficient than conventional Online Code Offloading and Scheduling and Adaptive Partitioning and Dynamic Selective Offloading.

Data driven hybrid edge computing-based hierarchical task guidance for efficient maritime escorting with multiple unmanned surface vehicles

The advancement of hardware and software technology makes multiple cooperative unmanned surface vehicles (USVs) utilized in maritime escorting with low cost and high efficiency. USVs can work as edge computing devices to locally and cooperatively perform heavy computational tasks without dependence of remote cloud servers. As such, we organize a team of USVs to escort a high value ship (e.g., mother ship) in a complex maritime environment with hostile intruder ships, where the significant challenge is to learn cooperation of USVs and assign each USV tasks to achieve optimal performance. To this end, in this paper, a hierarchical scheme is proposed for the USV team to guard a valuable ship, which belongs to problems of sparse rewards and long-time horizons in multi-agent reinforcement learning. The core idea utilized in the proposed scheme is centralized training with decentralized execution, where USVs learn policies to guard a high-value ship with extra shared environmental data from other USVs through communication. Specifically, the ships are divided into two groups, i.e., high-level ship and low-level USVs. The high-level ship optimizes decision-level policy to predict intercept points, while each low-level USV utilizes multi-agent reinforcement learning to learn task-level policy to intercept intruders. The hierarchical task guidance is exploited in maritime escorting, whereby high-level ship’s decision-level policy guides the training and execution of task-level policies of USVs. Simulation results and experiment analysis show that the proposed hierarchical scheme can efficiently execute the escort mission.

Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading

A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.

Privacy-preserving human action recognition as a remote cloud service using RGB-D sensors and deep CNN

Cloud-based expert systems are highly emerging nowadays. However, the data owners and cloud service providers are not in the same trusted domain in practice. For the sake of data privacy, sensitive data usually has to be encrypted before outsourcing which makes effective cloud utilization a challenging task. Taking this concern into account, we propose a novel cloud-based approach to securely recognize human activities. A few schemes exist in the literature for secure recognition. However, they suffer from the problem of constrained data and are vulnerable to re-identification attack, where advanced deep learning models are used to predict an object’s identity. We address these problems by considering color and depth data, and securing them using position based superpixel transformation. The proposed transformation is designed by actively involving additional noise while resizing the underlying image. Due to this, a higher degree of obfuscation is achieved. Further, in spite of securing the complete video, we secure only four images, that is, one motion history image and three depth motion maps which are highly saving the data overhead. The recognition is performed using a four stream deep Convolutional Neural Network (CNN), where each stream is based on pre-trained MobileNet architecture. Experimental results show that the proposed approach is the best suitable candidate in “security-recognition accuracy (%)” trade-off relation among other image obfuscation as well as state-of-the-art schemes. Moreover, a number of security tests and analyses demonstrate robustness of the proposed approach.

FogIoHT: A weighted majority game theory based energy-efficient delay-sensitive fog network for internet of health things

Internet of Health Things (IoHT) has become a demanding application of Internet of Things and cloud computing. In IoHT, the exchange and processing of health data are performed for monitoring the health status of patients. The health data collected using a body area network are stored and processed inside the cloud servers. The users can access the health data using their mobile devices as well as can receive health care advice. To reduce energy consumption and delay over the remote cloud servers, fog computing has been introduced. The health care system using fog computing is emerging due to the increasing need of energy and latency optimized health service provisioning. This article focuses on fog based Internet of Health Things for indoor as well as outdoor scenarios. In the proposed system the weighted majority game theory is used for selecting fog device in indoor and outdoor regions. Simulation results demonstrate that the proposed fog computing based system reduces the average delay, average jitter and energy consumption by approximately 15%, 20% and 15% respectively than the existing cloud only health care system.

A secure and scalable data integrity auditing scheme based on hyperledger fabric

As our society becomes smarter and more interconnected, more data such as those generated by Internet of Things (IoT) devices are stored remotely. These devices and services are generally externally owned and operated (e.g., commercial cloud servers). Hence, there has been interest in verifying the integrity of outsourced data, such as those stored in the remote cloud server, for example using schemes involving a third-party auditor (TPA). However, existing solutions involving TPA do not generally consider credibility and centralization, and such solutions may not be easily scalable. Thus, in this paper, we propose an efficient decentralized data integrity auditing scheme based on Hyperledger Fabric (HF-Audit), a popular consortium blockchain. Specifically, we use Hyperledger Fabric as a communication platform, where TPA can be dynamically selected for each auditing task. In order to improve the scalability of TPA, we design an efficient auditing protocol for data integrity based on bilinear pairing and commitments. Also, to improve auditing efficiency, we design two TPA selection algorithms under complete and incomplete information. Finally, we prove the security of the proposed approach, and evaluate its performance to demonstrate the utility of our proposed approach.

Public data integrity auditing without homomorphic authenticators from indistinguishability obfuscation

Cloud storage services allow users to outsource their data to remote cloud servers to relieve from the burden of local data storage and maintenance. Despite the benefits, data outsourcing has also made the data integrity protection in cloud storage a very challenging issue. Plenty of public auditing schemes have been proposed, which allow a third-party auditor to check the data integrity on behalf of users. However, most of these schemes are constructed on homomorphic authenticators, which require strong computation capability on the user side and incur high storage overhead. In this paper, a novel public data integrity auditing scheme is proposed from indistinguishability obfuscation. In our scheme, each user processes the entire data to be outsourced by using only symmetric key primitives, and the server only needs to store the outsourced data. In comparison with existing works, the proposed scheme frees users from the heavy burden on calculating the authenticators and reduces the storage overhead significantly. Moreover, we show the security of the proposed scheme with rigorous proofs. Finally, the performance analysis and comparison with existing works demonstrate the proposed scheme achieves better performance in terms of data processing cost on the user side and storage overhead.

Power and Delay Efficient Multilevel Offloading Strategies for Mobile Cloud Computing

Mobile cloud computing has introduced offloading to save the battery life of mobile devices. In mobile cloud computing optimization of power and delay for offloading has become a vital research scope. However, migration of the storage and computation from the mobile device to the remote cloud server enhances the delay and power consumption. To overcome this difficulty, cloudlet comes which is located nearby the mobile device. Since the cloudlet may not be able to fulfill all the offloading requests, sometimes remote public cloud server is used for the same. As a result the power and delay consumptions are increased. For solving this difficulty, private cloud server is used in our scheme along with the cloudlet and public cloud server. In this paper multilevel full and partial offloading strategies are proposed based on cloudlet, private and public cloud servers. The power and delay consumption in the proposed methods are determined and compared with the existing offloading methods. The theoretical and experimental analyses demonstrate that the proposed multilevel offloading methods are power and delay efficient. The simulation results show that the proposed multilevel full and partial offloading strategies reduce the power consumption by approximately 8–9% and 20% respectively than the existing methods.

A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection.

In this paper we proposed a wearable electrocardiogram (ECG) telemonitoring system for atrial fibrillation (AF) detection based on a smartphone and cloud computing. A wearable ECG patch was designed to collect ECG signals and send the signals to an Android smartphone via Bluetooth. An Android APP was developed to display the ECG waveforms in real time and transmit every 30 s ECG data to a remote cloud server. A machine learning (CatBoost)-based ECG classification method was proposed to detect AF in the cloud server. In case of detected AF, the cloud server pushed the ECG data and classification results to the web browser of a doctor. Finally, the Android APP displayed the doctor’s diagnosis for the ECG signals. Experimental results showed the proposed CatBoost classifier trained with 17 selected features achieved an overall F1 score of 0.92 on the test set (n = 7270). The proposed wearable ECG monitoring system may potentially be useful for long-term ECG telemonitoring for AF detection.

Enabling Secure Cross-Modal Retrieval Over Encrypted Heterogeneous IoT Databases With Collective Matrix Factorization

Significant volume of information of a broad variety (or modalities, such as image, audio, video, and text) is sensed and collected [such as those by the Internet of Things (IoT) devices] regularly (e.g., hourly). Such information is then analyzed to inform decision making, such as clinical diagnosis and product recommendation. Data with different representations may have the same semantic information, and there have been considerable efforts devoted to designing efficient searching approaches on objects with different modalities. However, multimodal data carry sensitive information, and maintaining privacy is crucial in our privacy-aware and interconnected society. In this article, we combine both the collective matrix factorization (CMF) and homomorphic encryption (HE) to construct an efficient and accurate scheme to facilitate cross-modal retrieval, without the loss of any sensitive information. Our scheme identifies the unified feature vectors for every object in the training set with different modalities and obtains the mapping matrices for out-of-sample objects. After the encryption process, these matrices are stored on the remote cloud server (CS). Hence, the server can calculate the secure, unified features for any query. In this article, we also built a privacy-preserving index structure using locality-sensitive hashing (LSH), which provides both security and efficiency. Performance evaluations demonstrate the potential for our proposed scheme in the real-world IoT applications.

Secure Outsourcing and Sharing of Cloud Data Using a User-Side Encrypted File System

Cloud computing is an emerging paradigm that aims to provide computing resources, massive data storage capacity and, flexible data sharing services. The explosive growth of data produced persuade business and users, driven by the cloud-top features, to outsource their data to the cloud storage systems. However, the confidentiality and integrity of outsourced sensitive data in remote cloud servers are becoming a major concern. Data must be encrypted prior to storing it in the, potentially untrustworthy cloud. Existing traditional encryption systems impose a heavy burden of managing files and encryption operations on data owners. They suffer from serious security, efficiency, and usability issues, and some schemes are inappropriate for protecting cloud data. In this paper, we introduce OutFS, a user-side encrypted file system, focused on providing a transparent encryption for stored and shared outsourced data. In OutFS, we utilize a hybrid encryption scheme structure based on symmetric and asymmetric methods. The key management is conveniently designed. In order to ensure robust data sharing security, the identity-based encryption scheme (IBE) is integrated with OutFS. OutFS is designed to preserve the integrity of outsourced file data and file system data structure. Analysis of performance and experimental results show that OutFS is efficient. It can achieve an average throughput of 8.8 MB/sec, and 10.5 MB/sec for writing and reading outsourced files. Security analysis indicates that OutFS is extremely secure and robust against attacks such as brute-force, eavesdropping, man-in-the-middle, and offline-dictionary attacks.

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multi-user, multi-edge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

IEEE Access Special Section Editorial: Information Centric Wireless Networking With Edge Computing for 5G and IoT

The advent of the Internet of Things (IoT) and Fifth Generation (5G) high-speed communication networks are expected not only to make it necessary to rapidly increase the number of communication nodes and generate large amounts of data but also to change the nature of the network to a more dynamic environment. However, the present network structure and the operation methods have various problems that need to be solved in order to cope with the aforementioned changes. Some issues, for example, are Transmission Control Protocol/Internet Protocol (TCP/IP)-based and end-to-end-based centralized and rigid network structure, the lack and allocation difficulties of IP addresses, increasing network traffic forwarding massive data to the cloud at remote locations for high-capacity data processing, as well as increasing delays in receiving effective analytic results from the remote cloud server.

ACO Based Container Placement for CaaS in Fog Computing

With the advancements in Internet of Things, there comes a plethora of applications that require real time response. To always process these applications in a remote cloud server is a time consuming task. This motivates the need of processing and storage capacities near to the data generating IOT sensors. One of such paradigm is that of Fog Computing. Fog Computing relies on the virtualization technology to offer compute and storage services. Containers being a lightweight preference reduce the startup time, migration time resulting in faster response to the application. This paper proposes an optimal placement policy of containers based on Ant Colony Optimization (ACO) with the primary aim to minimize the overall makespan of the tasks. This will help to achieve the desired real time response to applications ensuring QoS to the end users.

An Authorized Public Auditing Scheme for Dynamic Big Data Storage in Cloud Computing

Nowadays, to utilize the abundant resources of cloud computing, most enterprise users prefer to store their big data on cloud servers for sharing and utilization. However, storing data in remote cloud servers is out of user's control and exposes to lots of security problems such data availability, unauthorized access and data integrity, among which data integrity is a challenging and urgent task in cloud computing. Many auditing schemes have been proposed to check the integrity of data in cloud, but these schemes usually have some disadvantages. One is that these auditing schemes cannot check which block is corrupt when the data is not integrated. The other is that there's no efficient authenticated data structure helping to achieve accurate auditing when the data needs to update frequently. To solve the problems, we propose a public auditing scheme for dynamic big data storage in cloud computing. Firstly, we design a dynamic index table, in which no elements need to be moved in insertion or deletion update operations. Secondly, when data in cloud is not integrated, the third-party auditor can detect which block is corrupt. Finally, an authorization is employed between the third party and cloud servers to prevent denial of service attack. The theoretical analysis and the simulation results demonstrate that our scheme is more secure and efficient.

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

Attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications

With exploding growth in information technology (IT), numerous services and applications having enhanced capabilities are coming into picture with an aim to serve the users. Internet of things (IoT) along with its enabling cutting-edge technologies is establishing a scenario where these services can be utilised effectively. However, with large number of users and applications, it becomes challenging to safeguard the identifying information being transmitted to provide access to these services. This paper presents a refined version of an integrated attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications. System-wide attributes not only restrict the users to access the remote cloud services, but also ensure user anonymity. We also implement the proposed mechanism on ACPT and AVISPA tool for its validation and to verify its correctness. Moreover, we present an analysis of its security and performance efficiency on the basis of different parameters.