Reduce Storage Overhead Research Articles

A Novel Decentralized Group Key Management Scheme for Cloud-Based Vehicular IoT Networks

The vehicular internet of things (VIoT) network is connecting smart commuters to elevate traffic problems and effectiveness that help to improve driving conditions for vehicles. Every ITS-based vehicle OBU and commuter might be interchangeable and used to give access permission for the various services during transportation such as infotainment, emergency service, environment service, road condition, etc. These services are suffering from the 1-affect-n problem, scalability, memory resources, computation, and communication overhead. In this paper, the authors proposed a novel decentralized group key management protocol for cloud-based vehicular IoT networks (GVIoTNet) to solve the problem of 1-affect-n and scalability by using the decentralized approach of group key access, key generation, key distribution, key update among vehicles and commuters. Further, by using the master key encryption approach, the average rekeying minimization is 84.84% with the GroupIT scheme. The minimization of rekeying reduces storage overhead and improves computation as well as communication overhead.

LMT: Accurate and Resource-Scalable Slowdown Prediction

Multi-core processors suffer from inter-application interference which makes the performance of an application depend on the behavior of the applications it happens to be co-scheduled with. This results in performance variability, which is undesirable, and researchers have hence proposed numerous schemes for predicting the performance slowdown caused by inter-application interference. While a slowdown predictor's primary objective is to achieve high accuracy, it must typically also respect resource constraints. It is hence beneficial to be able to scale the resource consumption of the predictor, but state-of-the-art slowdown predictors are not resource-scalable. We hence propose to construct predictors using Linear Model Trees (LMTs) which we show to be accurate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> resource-scalable. More specifically, our 40-leaf-node LMT-40 predictor yields a 6.6% prediction error compared the 8.4% error of state-of-the-art GDP at similar storage overhead. In contrast, our LMT-10 predictor reduces storage overhead by 34.6% compared to GDP while only increasing prediction error to 9.4%.

Blockchain &amp; Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.

A Novel Image-Based Homomorphic Approach for Preserving the Privacy of Autonomous Vehicles Connected to the Cloud

Autonomous vehicles are taking a leap forward by performing operations without human intervention through continuous monitoring of their surroundings using multiple sensors. Images gathered through vehicle mounted cameras can be large, requiring specialized storage such as cloud. However, cloud data centres can be prone to security and privacy challenges. A partial image-based, homomorphic searchable encryption scheme is proposed, which uses pixel-level encryption to identify objects within encrypted images. The scheme provides Object-Trapdoor and Trapdoor-Image indistinguishability – as the trapdoors are probabilistic. The proposed scheme is deployed on a cloud data centre and tested over a real data set. The proposed scheme reduces storage overhead by approximately 20 times, and is 33 times more efficient compared to the generic Paillier homomorphic searchable encryption scheme. Security analysis demonstrates that the scheme maintains high levels of security and privacy.

A Lightweight authentication scheme for IoT against Rogue Base Station Attacks.

Internet of Things (IoT) devices supporting intelligent cloud applications such as healthcare for hospitals rely on connecting with local base stations and access points to provide rich data analysis and real-time services to users. Devices authenticate with local base stations and perform handover operations to connect with access points with higher signal strength. Attackers disguise as valid base stations and access points using publicly accessible SSID information connect with local IoT devices during the handover process and give rise to data integrity and privacy concerns. This paper proposes a lightweight authentication scheme for private blockchain-based networks for securing devices from rogue base stations during the handover process. An authentication certificate is designed for base stations and machines in local clusters using SHA256 and modulo operations for enabling quick handover operations. The keys assigned to each device and base station joining the network are hashed, and their sizes are reduced using modulo operations. Furthermore, the compressed key size forms a certificate, which is used by the machines and the base stations to authenticate mutually. In comparison with existing studies, the performance analysis of the proposed scheme is based on the transmission of three messages required for completing the authentication process. Evaluation based on the Communication Overhead demonstrates a minimum improvement of 99.30% fewer bytes exchanged during the handover process and 89.58% reduced Storage Overhead compared with existing studies.

Global repair bandwidth cost optimization of generalized regenerating codes in clustered distributed storage systems

In clustered distributed storage systems (CDSSs), one of the main design goals is minimizing the transmission cost during the failed storage nodes repairing. Generalized regenerating codes (GRCs) are proposed to balance the intra-cluster repair bandwidth and the inter-cluster repair bandwidth for guaranteeing data availability. The trade-off performance of GRCs illustrates that, it can reduce storage overhead and inter-cluster repair bandwidths simultaneously. However, in practical big data storage scenarios, GRCs cannot give an effective solution to handle the heterogeneity of bandwidth costs among different clusters for node failures recovery. This paper proposes an asymmetric bandwidth allocation strategy (ABAS) of GRCs for the inter-cluster repair in heterogeneous CDSSs. Furthermore, an upper bound of the achievable capacity of ABAS is derived based on the information flow graph (IFG), and the constraints of storage capacity and intra-cluster repair bandwidth are also elaborated. Then, a metric termed global repair bandwidth cost (GRBC), which can be minimized regarding of the inter-cluster repair bandwidths by solving a linear programming problem, is defined. The numerical results demonstrate that, maintaining the same data availability and storage overhead, the proposed ABAS of GRCs can effectively reduce the GRBC compared to the traditional symmetric bandwidth allocation schemes.

Collusion resistant secret sharing scheme for secure data storage and processing over cloud

Shamir secret sharing (SSS) is considered as a promising method for outsourcing the data securely due to its ability to support privacy preserving data processing while ensuring data availability. Major drawbacks of original SSS scheme are its susceptibility to collusion attack and high storage overhead. Hence in this paper, we first propose a modified SSS scheme (MSSS) which can resist collusion attack and provide adequate security even with two shares. However, the storage overhead of this scheme is high when it is extended to ensure data availability and integrity in cloud storage systems. Therefore, a modified ramp secret sharing (MRSS) with reduced storage overhead compared to MSSS scheme is also proposed in this paper. The proposed schemes can be employed for any privacy preserving data processing application which involve linear operations on the data. In this paper, in order to demonstrate the capability of proposed schemes to support privacy preserving data processing, Haar discrete wavelet transform (DWT) computation on medical images is considered as an example as DWT is widely used in feature extraction for disease diagnosis from pathological images. We present an algorithm for computing Haar DWT from medical image shares. The security of the proposed scheme is evaluated through mathematical cryptanalysis and resistance against various statistical attacks. The performance analysis shows that shared domain DWT offers same accuracy levels as that of plaintext domain.

CBase-EC: Achieving Optimal Throughput-Storage Efficiency Trade-Off Using Erasure Codes

Many distributed database systems that guarantee high concurrency and scalability adopt read-write separation architecture. Simultaneously, these systems need to store massive amounts of data daily, requiring different mechanisms for storing and accessing data, such as hot and cold data access strategies. Unlike distributed storage systems, the distributed database splits a table into sub-tables or shards, and the request frequency of each sub-table is not the same within a specific time. Therefore, it is not only necessary to design hot-to-cold approaches to reduce storage overhead, but also cold-to-hot methods to ensure high concurrency of those systems. We present a new redundant strategy named CBase-EC, using erasure codes to trade the performances of transaction processing and storage efficiency for CBase database systems developed for financial scenarios of the Bank. Two algorithms are proposed: the hot-cold tablets (shards) recognition algorithm and the hot-cold dynamic conversion algorithm. Then we adopt two optimization approaches to improve CBase-EC performance. In the experiment, we compare CBase-EC with three-replicas in CBase. The experimental results show that although the transaction processing performance declined by no more than 6%, the storage efficiency increased by 18.4%.

2DInpaint: A novel privacy-preserving scheme for image inpainting in an encrypted domain over the cloud

The low cost, agility, and mobility of cloud services for processing and storage data have attracted user’s attention today. Untrusted third parties support these services, and users are always concerned about utilizing them for personal data. Addressing these data-privacy issues for image inpainting over the cloud infrastructure(s), we propose a model, 2DInpaint, to perform image inpainting by protecting image information from an eavesdropping adversary. Inpainting is a technique for modifying an image in an undetectable manner with applications ranging from restoration of damaged photographs to object-removal and replacement of lost blocks in image coding and transmission. It can be accomplished by propagating the information in the isophotes direction of the desired region(s) from the neighborhood. Performing this propagation when the image is in the encrypted domain (ED) is a challenging dilemma. The challenge is addressed by employing a modified version of 2D-bicubic interpolation over the region to be inpainted in ED. The ramp secret sharing scheme is utilized to secure image information and to reduce storage overhead over the cloud server. 2DInpaint is proved to be information-theoretical secure in a probabilistic viewpoint and through various cryptographic attacks. The qualitative and quantitative results of 2DInpaint are analyzed under the scenarios of classical image inpainting, object-removal, and text-removal, and compared with the schemes in the plain domain. Moreover, no limitations related to the topology of the region to be inpainted are required using our approach. To the best of our knowledge, 2DInpaint is the first move towards image inpainting in the ED.

Reputation Based Fault Tolerance in Blockchain Networks

The Blockchain technology brings a rapid growth in the industry, It emphasizes the service to lead the complexity of software and malicious attack in the network. This technology is used to monitor the highly vulnerable services and it is used to increase the complexity of the warehouse data. It assures the security and consistency of data, The warehouse data has been replicated the availability and the enhancement of security in the services. This technology originated from internet sector as a decentralized, distributed ledger for data transaction. Nowadays, it is visualised as a backbone or frame work for decentralized data processing in open source network. Blockchain uses variety of consensus protocol which is reliable for nodes and communication resources that is used for data consistency. Byzantine fault tolerance algorithm has been proposed for computational cost and security also for consensus efficiency. This paper deals by proposing practical byzantine fault tolerance on edge computing networks paves away for reducing storage overhead also security purpose on edge devices. The proposed model is simulated in the constrain environment and the results are discussed. It shows that the proposed method has increase the availability and security of the stored data.

Data encoding and cost optimised distribution for efficient and secure storage in cloud federation

Cloud federation is the future paradigm of computing which can reduce cost and avoid vendor lock-in problem. Cloud providers are now collaborating for increasing revenue and building trust among customers and indirectly help in better resource utilisation of every service provider in the federation. In the proposed framework all collaborating service providers share their storage resources and use single sign-on federated identity management model which will reduce the overhead of maintaining multiple credentials and strengthen the authentication process. Initially, files are encrypted with the secret key created with client id, session token and a random number which will improve the data security and based on the service level selected for files erasure codes are applied, which will reduce storage overhead as compared to the traditional replication techniques. Hence overall reliability, availability and security of data stored in the cloud federation is improved.

Fingerprint-Based Localization for Massive MIMO-OFDM System With Deep Convolutional Neural Networks

Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint based location for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with deep convolutional neural networks(DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. The performance of the proposed DCNN localization method is evaluated via simulation performed in a geometry-based ray tracing signal propagation scene. Numerical results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.

Blockchain & Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.

Data Encoding and Cost Optimized Distribution for Efficient and Secure Storage in Cloud Federation

Cloud federation is the future paradigm of computing which can reduce cost and avoid vendor lock-in problem. Cloud providers are now collaborating for increasing revenue and building trust among customers and indirectly help in better resource utilisation of every service provider in the federation. In the proposed framework all collaborating service providers share their storage resources and use single sign-on federated identity management model which will reduce the overhead of maintaining multiple credentials and strengthen the authentication process. Initially, files are encrypted with the secret key created with client id, session token and a random number which will improve the data security and based on the service level selected for files erasure codes are applied, which will reduce storage overhead as compared to the traditional replication techniques. Hence overall reliability, availability and security of data stored in the cloud federation is improved.

CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data.

CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.

Single-Site Localization Based on a New Type of Fingerprint for Massive MIMO-OFDM Systems

Localization for mobile devices is drawing increasing interest from both industry and academia as location-based services spring up in recent years. For rich scattering environments, such as urban areas and indoor corridors, fingerprint-based localization techniques are very promising. In this paper, we propose a fingerprint-based single-site localization method for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. A new angle delay channel power matrix fingerprint is extracted from instantaneous channel state information by taking full advantage of the high resolution in the angle and delay domains for massive MIMO-OFDM systems. A new fingerprint similarity criterion is proposed to facilitate localization. Based on the new criterion, an efficient location estimation method is developed. To reduce storage overhead and matching complexity, we also propose a fingerprint compression method and a two-stage fingerprint clustering algorithm for database preprocessing. Numerical results demonstrate the desirable performance of the proposed localization method.

A New Adaptive Coding Selection Method for Distributed Storage Systems

Erasure codes, such as Reed–Solomon (RS) codes and local reconstruction codes (LRCs), are being increasingly adopted in distributed storage systems since they offer lower redundancy than data replication. While these codes significantly save storage space, they can incur large I/O overhead and network traffic in reconstructing unavailable data. Most existing storage systems use replication for hot data and an erasure code for warm and cold data, thereby achieving a good tradeoff between storage overhead and recovery performance. However, these storage systems do not take the access characteristics of data into account and tend to use only an erasure code, which hinders the possibility of reducing storage overhead and recovery cost. In this paper, we propose a new adaptive coding selection method that instead uses multiple LRCs for warm data. The LRCs are selected based on the access characteristics of the data. Each time a file is accessed, we assume that each of the involved data blocks is unavailable, in turn. It is necessary to calculate the I/O cost to recover unavailable blocks for different LRCs. The sum of the I/O costs for each LRC is calculated, and the LRC with the minimal I/O cost is selected for warm data. For cold data, we use an RS code that is optimized for storage overhead to reduce the storage burden. Our method is implemented on the top of the Hadoop distributed file system. Evaluations show that it reduces the storage overhead by up to 5% and the reconstruction traffic by up to 22%.

Efficient Deduplication in a Distributed Primary Storage Infrastructure

A large amount of duplicate data typically exists across volumes of virtual machines in cloud computing infrastructures. Deduplication allows reclaiming these duplicates while improving the cost-effectiveness of large-scale multitenant infrastructures. However, traditional archival and backup deduplication systems impose prohibitive storage overhead for virtual machines hosting latency-sensitive applications. Primary deduplication systems reduce such penalty but rely on special cluster filesystems, centralized components, or restrictive workload assumptions. Also, some of these systems reduce storage overhead by confining deduplication to off-peak periods that may be scarce in a cloud environment. We present DEDIS, a dependable and fully decentralized system that performs cluster-wide off-line deduplication of virtual machines’ primary volumes. DEDIS works on top of any unsophisticated storage backend, centralized or distributed, as long as it exports a basic shared block device interface. Also, DEDIS does not rely on data locality assumptions and incorporates novel optimizations for reducing deduplication overhead and increasing its reliability. The evaluation of an open-source prototype shows that minimal I/O overhead is achievable even when deduplication and intensive storage I/O are executed simultaneously. Also, our design scales out and allows collocating DEDIS components and virtual machines in the same servers, thus, sparing the need of additional hardware.

DiVers: An erasure code based storage architecture for versioning exploiting sparsity

We propose a differential versioning based data storage (DiVers) architecture for distributed storage systems, which relies on a novel erasure coding technique that exploits sparsity across versions. The emphasis of this work is to demonstrate how sparsity exploiting codes (SEC), originally designed for I/O optimization, can be extended to significantly reduce storage overhead in a repository of versioned data. In addition to facilitating reduced storage, we address some key reliability aspects for DiVers such as (i) mechanisms to deploy the coding technique with arbitrarily varying size of data across versions, and (ii) investigating the right allocation strategy for the encoded blocks over a network of distributed nodes across different versions so as to achieve the best fault tolerance. We also discuss system issues related to the management of data structures for accessing and manipulating the files over the differential versions.

Recursive Trees for Practical ORAM

Abstract We present a new, general data structure that reduces the communication cost of recent tree-based ORAMs. Contrary to ORAM trees with constant height and path lengths, our new construction r-ORAM allows for trees with varying shorter path length. Accessing an element in the ORAM tree results in different communication costs depending on the location of the element. The main idea behind r-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees. While this approach results in a worst-case access cost (tree height) at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs. Besides reducing communication cost, r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM’s client memory type. To prove r-ORAM’s soundness, we conduct a detailed overflow analysis. r-ORAM’s recursive approach is general in that it can be applied to all recent tree ORAMs, both constant and poly-log client memory ORAMs. Finally, we implement and benchmark r-ORAM in a practical setting to back up our theoretical claims.