IEEE 2014 JAVA
IEEE 2014 : A Secure Client Side Deduplication Scheme in Cloud Storage Environments
Project Price: Contact US
Abstract—Recent years have witnessed the trend of leveraging cloud-based services for large scale content storage, processing, and distribution. Security and privacy are among top concerns for the public cloud environments. Towards these security challenges, we propose and implement, on Open Stack Swift, a new client-side deduplication scheme for securely storing and sharing outsourced data via the public cloud. The originality of our proposal is twofold. First, it ensures better confidentiality towards unauthorized users. That is, every client computes a per data key to encrypt the data that he intends to store in the cloud. As such, the data access is managed by the data owner. Second, by integrating access rights in metadata file, an authorized user can decipher an encrypted file only with his private key.
IEEE 2014: Adaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors
Project Price: Contact US
Abstract—compared to traditional distributed computing like Grid system, it is non-trivial to optimize cloud task’s execution Performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: (1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and host load prediction errors. With such state-of the-art bounds, the worst-case task execution performance is predictable, which can improve the Quality of Service in turn. (2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. (3)We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks.
IEEE 2014:An Efficient Information Retrieval Approach for Collaborative Cloud Computing
Project Price: Contact US
Abstract—The collaborative cloud computing (CCC) which is collaboratively supported by various organizations (Google, IBM, AMAZON, MICROSOFT) offers a promising future for information retrieval. Human beings tend to keep things simple by moving the complex aspects to computing. As a consequence, we prefer to go to one or a limited number of sources for all our information needs. In contemporary scenario where information is replicated, modified (value added), and scattered geographically; retrieving information in a suitable form requires lot more effort from the user and thus difficult. For instance, we would like to go directly to the source of information and at the same time not to be burdened with additional effort. This is where, we can make use of learning systems (Neural Network based) that can intelligently decide and retrieve the information that we need by going directly to the source of information. This also, reduces single point of failure and eliminates bottlenecks in the path of information flow, Reduces the Time delay and it provide remarkable ability to overcome from traffic conjection complicated patterns. It makes Efficient information retrieval approach for collaborative cloud computing. both secure and verifiable, without relying on random oracles. Finally, we show an implementation
IEEE 2014: Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
Project Price: Contact US
Project Price: Contact US
Abstract—With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.
IEEE2014:Compatibility-aware Cloud Service Composition under Fuzzy Preferences of Users
Project Price: Contact US
Abstract—When a single Cloud service (i.e., a software image and a virtual machine), on its own, cannot satisfy all the user requirements, a composition of Cloud services is required. Cloud service composition, which includes several tasks such as discovery, compatibility checking, selection, and deployment, is a complex process and users find it difficult to select the best one among the hundreds, if not thousands, of possible compositions available. Service composition in Cloud raises even new challenges caused by diversity of users with different expertise requiring their applications to be deployed across difference geographical locations with distinct legal constraints. The main difficulty lies in selecting a combination of virtual appliances (software images) and infrastructure services that are compatible and satisfy a user with vague preferences. Therefore, we
Project Price: Contact US
Present a framework and algorithms which simplify Cloud service composition for unskilled users. We develop an ontology based approach to analyze Cloud service compatibility by applying reasoning on the expert knowledge. In addition, to minimize effort of users in expressing their preferences, we apply combination of evolutionary algorithms and fuzzy logic for composition optimization. This lets users express their needs in linguistics terms which brings a great comfort to them compared to systems that force users to assign exact weights for all preferences.
IEEE 2014:Consistency as a Service: Auditing Cloud Consistency
Project Price: Contact US
Project Price: Contact US
Abstract—Cloud storage services have become commercially popular due to their overwhelming advantages. To provide ubiquitous always-on access, a cloud service provider (CSP) maintains multiple replicas for each piece of data on geographically distributed servers. A key problem of using the replication technique in clouds is that it is very expensive to achieve strong consistency on a worldwide scale. In this paper, we first present a novel consistency as a service (CaaS) model, which consists of a large data cloud and multiple small audit clouds. In the CaaS model, a data cloud is maintained by a CSP, and a group of users that constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not. We propose a two-level auditing architecture, which only requires a loosely synchronized clock in the audit cloud. Then, we design Algorithms to quantify the severity of violations with two metrics: the commonality of violations, and the staleness of the value of a read. Finally, we devise a heuristic auditing strategy (HAS) to reveal as many violations as possible. Extensive experiments were performed using a combination of simulations and real cloud deployments to validate HAVE.
IEEE 2014:Data Similarity-Aware Computation Infrastructure for the Cloud
Project Price: Contact US
Abstract—the cloud is emerging for scalable and efficient cloud services. To meet the needs of handling massive data and decreasing data migration, the computation infrastructure requires efficient data placement and proper management for cached data. In this paper, we propose an efficient and cost-effective multilevel caching scheme, called MERCURY, as computation infrastructure of the cloud. The idea behind MERCURY is to explore and exploit data similarity and support efficient data placement. To accurately and efficiently capture the data similarity, we leverage a low-complexity locality-sensitive hashing (LSH). In our design, in addition to the problem of space inefficiency, we identify that a conventional LSH scheme also suffers from the problem of homogeneous data placement. To address these two problems, we design a novel multi core-enabled locality-sensitive hashing (MC-LSH) that accurately captures the differentiated similarity across data. The similarity-aware MERCURY, hence, partitions data into the L1 cache, L2 cache, and main memory based on their distinct localities, which help optimize cache utilization and minimize the pollution in the last-level cache. Besides extensive evaluation through simulations, we also implemented MERCURY in a system. Experimental results based On real-world applications and data sets demonstrate the efficiency and efficacy of our proposed schemes.
Project Price: Contact US
IEEE 2014:Maximizing Revenue with Dynamic Cloud Pricing: The Infinite Horizon Case
Project Price: Contact US
Project Price: Contact US
IEEE 2014:Facilitating Document Annotation using Content and Querying Value
Project Price: Contact US
Project Price: Contact US
Abstract—A large number of organizations today generate and share textual descriptions of their products, services, and actions. Such collections of textual data contain significant amount of structured information, which remains buried in the unstructured text. While information extraction algorithms facilitate the extraction of structured relations, they are often expensive and inaccurate, especially when operating on top of text that does not contain any instances of the targeted structured information.We present a novel alternative approach that facilitates the generation of the structured metadata by identifying documents that are likely to contain information of interest and this information is going to be subsequently useful for querying the database. Our approach relies on the idea that humans are more likely to add the necessary metadata during creation time, if prompted by the interface; or that it is much easier for humans (and/or algorithms) to identify the metadata when such information actually exists in the document, instead of naively prompting users to fill in forms with information that is not available in the document. As a major contribution of this paper, we present algorithms that identify structured attributes that are likely to appear within the document, by jointly utilizing the content of the text and the query workload. Our experimental evaluation shows that our approach generates superior results compared to approaches that rely only on the textual content or only on the query workload, to identify attributes of interest.
Project Price: Contact US
Abstract—In cryptography, secure channels enable the confidential and authenticated message exchange between authorized users. A generic approach of constructing such channels is by combining an encryption primitive with an authentication primitive (MAC). In this work, we introduce the design of a new cryptographic primitive to be used in the construction of secure channels. Instead of using general purpose MACs, we propose the deployment of special purpose MACs, named E-MACs. The main motivation behind this work is the observation that, since the message must be both encrypted and authenticated, there might be some redundancy in the computations performed by the two primitives. Therefore, removing such redundancy can improve the efficiency of the overall composition. Moreover, computations performed by the encryption algorithm can be further utilized to improve the security of the authentication algorithm. In particular, we will show how E-MACs can be designed to reduce the amount of computation required by standard MACs based on universal hash functions, and show how E-MACs can be secured against key-recovery attacks.
IEEE 2014:Optimal Multicast Capacity and Delay Tradeoffs in MANETs
Project Price: Contact US
Project Price: Contact US
Abstract—In this paper, we give a global perspective of multicast capacity and delay analysis in Mobile Ad Hoc Networks (MANETs). Specifically, we consider four node mobility models: (1) two-dimensional i.i.d. mobility, (2) two-dimensional hybrid random walk, (3) one-dimensional i.i.d. mobility, and (4) one-dimensional hybrid random walk. Two mobility time-scales are investigated in this paper: (i) Fast mobility where node mobility is at the same time-scale as data transmissions; (ii) Slow mobility where node mobility is assumed to occur at a much slower time-scale than data transmissions. Given a delay constraint D, we first characterize the optimal multicast capacity for each of the eight types of mobility models, and then we develop a scheme that can achieve a capacity-delay tradeoff close to the upper bound up to a logarithmic factor. In addition, we also study heterogeneous networks with infrastructure support.
IEEE 2014:Reliable Energy Efficient Routing Algorithms in Wireless Ad Hoc Networks
Project Price: Contact US
Project Price: Contact US
IEEE 2014:Automating the Integration of Clinical Studies into Medical Ontologies
Project Price: Contact US
Project Price: Contact US
Abstract—A popular approach to knowledge extraction from clinical databases is to first define ontology (A formal specification of how to represent relationships among objects, concepts, and other entity belonging to a particular area of human experience or knowledge.) of the concepts one wishes to model and subsequently, use these concepts to test various hypotheses and make predictions about a person’s future health and wellbeing. The challenge for medical experts is in the time taken to map between their concepts/hypothesis (idea/foundation) and information contained within clinical studies. Presently, most of this work is performed manually. We have developed a method to generate links between Risk Factors in a medical ontology and the questions and result data in longitudinal studies. This can then be exploited to express complex queries based on domain concepts, to extract knowledge from external studies.
IEEE 2014:Building a Scalable System for Stealthy P2P-Botnet Detection
Project Price: Contact US
Project Price: Contact US
Abstract—Peer-to-peer (P2P) botnets have recently been adopted by botmasters for their resiliency against take-down efforts. Besides being harder to take down, modern botnets tend to be stealthier in the way they perform malicious activities, making current detection approaches ineffective. In addition, the rapidly growing volume of network traffic calls for high scalability of detection systems. In this paper, we propose a novel scalable botnet detection system capable of detecting stealthy P2P botnets. Our system first identifies all hosts that are likely engaged in P2P communications. It then deriv es statistical fingerprints to profile P2P traffic and further distinguish between P2P botnet traffic and legitimate P2P traffic. The parallelized computation with bounded complexity makes scalability a built-in feature of our system. Extensive evaluation has demonstrated both high detection accuracy and great scalability of the proposed system
IEEE 2014: STARS: A Statistical Traffic Pattern Discovery System for Anonymous MANET communications
Project Price: Contact US
Abstract—Anonymous MANET routing relies on techniques such as re-encryption on each hop to hide end-to-end communication relations. However, passive signal detectors and traffic analyzers can still retrieve sensitive information from PHY and MAC layers to derive end-to-end communication relations through statistical traffic analysis. In this paper, we propose a Statistical Traffic pattern discovery System (STARS) based on Eigen analysis which can greatly improve the accuracy to derive traffic patterns in MANETs. A STAR intends to find out the sources and destinations of captured packets and to discover the end-to-end communication relations. The proposed approach is purely passive. It does not require analyzers to be actively involved in MANET transmissions and to possess encryption keys to decrypt traffic. We present theoretical models as well as extensive simulations to demonstrate our solutions.
IEEE 2014:Enabling Data Integrity Protection in Regenerating-Coding-Based Cloud Storage
Project Price: Contact US
Project Price: Contact US
Abstract—to protect outsourced data in cloud storage against corruptions, enabling integrity protection, fault tolerance, and efficient recovery for cloud storage becomes critical. Regenerating codes provide fault tolerance by striping data across multiple servers, while using less repair traffic than traditional erasure codes during failure recovery. Therefore, we study the problem of remotely checking the integrity of regenerating-coded data against corruptions under a real-life cloud storage setting. We Design and implement a practical data integrity protection (DIP) scheme for a specific regenerating code, while preserving the intrinsic properties of fault tolerance and repair traffic saving. Our DIP scheme is designed under a Byzantine adversarial model, and enables a client to feasibly verify the integrity of random subsets of outsourced data against general or malicious corruptions. It works under the simple assumption of thin-cloud storage and allows different parameters to be fine-tuned for the performance-security trade-off. We implement and evaluate the overhead of our DIP scheme in a real cloud storage test bed under different parameter choices. We demonstrate that remote integrity checking can be feasibly integrated into regenerating codes in practical deployment.
IEEE 2014:Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage
Project Price: Contact US
Abstract—Data sharing is an important functionality in cloud storage. In this article, we show how to securely, efficiently, and flexibly share data with others in cloud storage. We describe new public-key cryptosystems which produce constant-size cipher texts such that efficient delegation of decryption rights for any set of cipher texts are possible. The novelty is that one can aggregate any set of secret keys and make them as compact as a single key, but encompassing the power of all the keys being aggregated. In other words, the secret key holder can release a constant-size aggregate key for flexible choices of cipher text set in cloud storage, but the other encrypted files outside the set remain confidential. This compact aggregate key can be conveniently sent to others or be stored in a smart card with very limited secure storage. We provide formal security analysis of our schemes in the standard model. We also describe other application of our schemes. In particular, our schemes give the first public-key patient-controlled encryption for flexible hierarchy, which was yet to be known.
Project Price: Contact US
IEEE 2014:Low-Carbon Routing Algorithms for Cloud Computing Services in IP-over-WDM Networks
Project Price: Contact US
Project Price: Contact US
Abstract—Energy consumption in telecommunication networks keeps growing rapidly, mainly due to emergence of new Cloud Computing (CC) services that need to be supported by large data centers that consume a huge amount of energy and, in turn, cause the emission of enormous quantity of CO2. Given the decreasing availability of fossil fuels and the raising concern about global warming, research is now focusing on novel “low-carbon” telecom solutions. E.g., based on today telecom technologies, data centers can be located near renewable energy plants and data can then be effectively transferred to these locations via reconfigurable optical networks, based on the principle that data can be moved more efficiently than electricity. This paper focuses on how to dynamically route on-demand optical circuits that are established to transfer energy-intensive data processing towards data centers powered with renewable energy. Our main contribution consists in devising two routing algorithms for connections supporting CC services, aimed at minimizing the CO2 emissions of data centers by following the current availability of renewable energy (Sun and Wind). The trade-off with energy consumption for the transport equipments is also considered. The results show that relevant reductions, up to about 30% in CO2 emissions can be achieved using our approaches compared to baseline shortest path- based routing strategies, paying off only a marginal increase in terms of network blocking probability
IEEE 2014: A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers
Project Price: Contact US
Abstract—this paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.
Project Price: Contact US
Abstract—this paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.
IEEE 2014:NCCloud: A Network-Coding-Based Storage System in a Cloud-of-Clouds
Project Price: Contact US
Abstract—to provide fault tolerance for cloud storage, recent studies propose to stripe data across multiple cloud vendors. However, if a cloud suffers from a permanent failure and loses all its data, we need to repair the lost data with the help of the other surviving clouds to preserve data redundancy. We present a proxy-based storage system for fault-tolerant multiple-cloud storage called NCCloud, which achieves cost-effective repair for a permanent single-cloud failure. NCCloud is built on top of a network-coding-based storage scheme called the functional minimum-storage regenerating (FMSR) codes, which maintain the same fault tolerance and data redundancy as in traditional erasure codes (e.g., RAID-6), but use less repair traffic and hence incur less monetary cost due to data transfer. One key design feature of our FMSR codes is that we relax the encoding requirement of storage nodes during repair, while preserving the benefits of network coding in repair. We implement a proof-of-concept prototype of NCCloud and deploy it atop both local and commercial clouds. We validate that FMSR codes provide significant monetary cost savings in repair over RAID-6 codes, while having comparable response time performance in normal cloud storage operations such as upload/download.
Project Price: Contact US
IEEE 2014:Integrity Verification in Multi-Cloud Storage Using Cooperative Provable Data Possession
Project Price: Contact US
Abstract- Storage outsourcing in cloud computing is a rising trend which prompts a number of interesting security issues. Provable data possession (PDP) is a method for ensuring the integrity of data in storage outsourcing. This research addresses the construction of efficient PDP which called as Cooperative PDP (CPDP) mechanism for distributed cloud storage to support data migration and scalability of service, which considers the existence of multiple cloud service providers to collaboratively store and maintain the clients’ data. Cooperative PDP (CPDP) mechanism is based on homomorphic verifiable response, hash index hierarchy for dynamic scalability, cryptographic encryption for security. Moreover, it proves the security of scheme based on multi-prover zero knowledge proof system, which can satisfy knowledge soundness, completeness, and zero-knowledge properties. This research introduces lower computation and communication overheads in comparison with non-cooperative approaches.
Project Price: Contact US
IEEE 2014:Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multi core Server Processors across Clouds and Data Centers
Project Price: Contact US
Abstract—for multiple heterogeneous multi core server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large scale server systems in current and future data centers. The multi core processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multi core server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multi core server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.
Project Price: Contact US
IEEE 2014:Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud
Project Price: Contact US
Abstract—with cloud storage services, it is commonplace for data to be not onsly stored in the cloud, but also shared across multiple users. However, public auditing for such shared data — while preserving identity privacy — remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data.
Project Price: Contact US
IEEE 2014:Towards Differential Query Services in Cost-Efficient Clouds
Project Price: Contact US
Abstract—Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost-efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server without leaking any information. The main drawback is that it will cause a heavy querying overhead incurred on the cloud and thus goes against the original intention of cost efficiency. In this paper, we present three efficient information retrieval for ranked query (EIRQ) schemes to reduce querying overhead incurred on the cloud. In EIRQ, queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. A user can retrieve files on demand by choosing queries of different ranks. This feature is useful when there are a large number of matched files, but the user only needs a small subset of them. Under different parameter settings, extensive evaluations have been conducted on both analytical models and on a real cloud environment, in order to examine the effectiveness of our schemes.
Project Price: Contact US
IEEE 2014:Scalable Distributed Service Integrity Attestation for Software-as-a-Service Clouds
Project Price: Contact US
Project Price: Contact US
Abstract—Software-as-a-Service (SaaS) cloud systems enable application service providers to deliver their applications via massive cloud computing infrastructures. However, due to their sharing nature, SaaS clouds are vulnerable to malicious attacks. In this paper, we present IntTest, a scalable and effective service integrity attestation framework for SaaS clouds. Int Test provides a novel integrated attestation graph analysis scheme that can provide stronger attacker pinpointing power than previous schemes. Moreover, IntTest can automatically enhance result quality by replacing bad results produced by malicious attackers with good results produced by benign service providers. We have implemented a prototype of the IntTest system and tested it on a production cloud computing infrastructure using IBM System S stream processing applications. Our experimental results show that IntTest can achieve higher attacker pinpointing accuracy than existing approaches. IntTest does not require any special hardware or secure kernel support and imposes little performance impact to the application, which makes it practical for large scale cloud systems.
IEEE 2014:QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems.
Project Price: Contact US
Abstract—Cloud computing provides scalable computing and storage resources. More and more data-intensive applications are developed in this computing environment. Different applications have different quality-of-service (QoS) requirements. To continuously support the QoS requirement of an application after data corruption, we propose two QoS-aware data replication (QADR) algorithms in cloud computing systems. The first algorithm adopts the intuitive idea of high-QoS first-replication (HQFR) to perform data replication. However, this greedy algorithm cannot minimize the data replication cost and the number of QoS-violated data replicas. To achieve these two minimum objectives, the second algorithm transforms the QADR problem into the well-known minimum-cost maximum-flow (MCMF) problem. By applying the existing MCMF algorithm to solve the QADR problem, the second algorithm can produce the optimal solution to the QADR problem in polynomial time, but it takes more computational time than the first algorithm. Moreover, it is known that a cloud computing system usually has a large number of nodes. We also propose node combination techniques to reduce the possibly large data replication time. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed algorithms in the data replication and recovery.
Project Price: Contact US
IEEE 2014:Public Auditing for Shared Data with Efficient User Revocation in the Cloud
Project Price: Contact US
Abstract—with data services in the cloud, users can easily modify and share data as a group. To ensure data integrity can be audited publicly, users need to compute signatures on all the blocks in shared data. Different blocks are signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks, which were previously signed by this revoked user, must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is Inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.
Project Price: Contact US
IEEE 2014:Ensuring Integrity Proof in Hierarchical Attribute Encryption Scheme using Cloud Computing
Project Price: Contact US
Abstract— It has been widely observed that the concept of cloud computing has become one of the major theory in the world of IT industry. Data owners decide to release their burden of storing and maintaining the data locally by storing it over the cloud. Cloud storage moves the owner’s data to large data centers which are remotely located on which data owner does not have any control. However, this unique feature of the cloud poses many new security challenges. One of the important concerns that need to be addressed is access control of outsourced data in cloud. Numbers of schemes have been proposed to achieve the access control of outsourced data like hierarchical attribute set based encryption [HASBE] by extending cipher-text-policy attribute set based encryption [CP-ABE]. Even though HASBE scheme achieves scalability, flexibility and fine grained access control, it fails to prove the data integrity in the cloud. However, the fact that owners no longer have physical possession of data indicates that they are facing a potentially formidable risk for missing or corrupted data, because sometimes the cloud service provider modifies or deletes the data in the cloud without the knowledge or permission of data owner. Hence in order to avoid this security risk, in this paper we propose a method which gives data integrity proof for HASBE scheme. Data integrity refers to maintaining and assuring the accuracy and consistency of data over its entire life-cycle.
Project Price: Contact US
IEEE 2014 : Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data
Project Price: Contact US
Abstract—With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of “coordinate matching,” i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use “inner product similarity” to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication
Project Price: Contact US
IEEE 2014:Proactive Workload Management in Hybrid Cloud Computing
Project Price: Contact US
Abstract—The hindrances to the adoption of public cloud computing services include service reliability, data security and privacy, regulation compliant requirements, and so on. To address those concerns, we propose a hybrid cloud computing model which users may adopt as a viable and cost-saving methodology to make the best use of public cloud services along with their privately-owned (legacy) data centers. As the core of this hybrid cloud computing model, an intelligent workload factoring service is designed for proactive workload management. It enables federation between on- and off-premise infrastructures for hosting Internet-based applications, and the intelligence lies in the explicit segregation of base workload and flash crowd workload, the two naturally different components composing the application workload. The core technology of the intelligent workload factoring service is a fast frequent data item detection algorithm, which enables factoring incoming requests not only on volume but also on data content, upon a changing application data popularity. Through analysis and extensive evaluation with real-trace driven simulations and experiments on a hybrid test bed consisting of local computing platform and Amazon Cloud service platform, we showed that the proactive workload management technology can enable reliable workload prediction in the base workload zone (with simple statistical methods), achieve resource efficiency (e.g., 78% higher server capacity than that in base workload zone) and reduce data cache/replication overhead (up to two orders of magnitude) in the flash crowd workload zone, and react fast (with an X2 speed-up factor) to the changing application data popularity upon the arrival of load spikes.
Project Price: Contact US
IEEE 2013 DOT NET
Mona: Secure
Multi-Owner Data Sharing for Dynamic Groups in the Cloud
Project Price: Contact US
ABSTRACT:With the character of low maintenance, cloud computing provides an economical and efficient solution for sharing group resource among cloud users.Unfortunately, sharing data in a multi-owner manner while preserving data and identity privacy from an un-trusted cloud is still a challenging issue, due to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of our scheme in experiments.
Privacy
Preserving Delegated Access Control
in Public Clouds
Project Price: Contact US
ABSTRACT:Current approaches to enforce fine-grained access control on confidential data hosted in the cloud are based on fine-grained encryption of the data. Under such approaches, data owners are in charge of encrypting the data before uploading them on the cloud and re-encrypting the data whenever user credentials or authorization policies change. Data owners thus incur high communication and computation costs. A better approach should delegate the enforcement of fine-grained access control to the cloud, so to minimize the overhead at the data owners, while assuring data confidentiality from the cloud. We propose an approach, based on two layers of encryption that addresses such requirement. Under our approach, the data owner performs a coarse-grained encryption, whereas the cloud performs a fine-grained encryption on top of the owner encrypted data. A challenging issue is how to decompose access control policies (ACPs) such that the two layer encryption can be performed. We show that this problem is NP-complete and propose novel optimization algorithms. We utilize an efficient group key management scheme that supports expressive ACPs. Our system assures the confidentiality of the data and preserves the privacy of users from the cloud while delegating most of the access control enforcement to the cloud.
Local Directional Number Pattern for Face Analysis: Face
and Expression Recognition
Project Price: Contact US
Adding Persuasive features in Graphical Password To increase the capacity of KBAM
Project Price: Contact US
ABSTRACT:Most of the existing authentication system has certain drawbacks for that reason graphical passwords are most preferable authentication system where users click on images to authenticate themselves. An important usability goal of an authentication system is to support users for selecting the better password. User creates memorable password which is easy to guess by an attacker and strong system assigned passwords are difficult to memorize. So researchers of modern days gone through different alternative methods and conclude that graphical passwords are most preferable authentication system. The proposed system combines the existing cued click point technique with the persuasive feature to influence user choice, encouraging user to select more random click point which is difficult to guess.
An Encryption and Decryption Algorithm for Image Based on
DNA
DNA
Project Price: Contact US
ABSTRACT: A novel image encryption algorithm based on DNA sequence addition operation. This initiation and increasing escalation of Internet has caused the information to be paperless and the makeover into electronic compared to the conventional digital image distribution. In this paper we proposed and implement four phase. First phase, image is renovating into binary matrix. Afterward matrix is apportioning into equal blocks. Second phase, each block is then encoded into DNA sequences and DNA sequence addition operation used to add these blocks. For that result of added matrix is achieved by using two Logistic maps. At the time of decoding the DNA sequence matrix is complemented and we encrypt that result by using DES then we get encrypted image. Our paper includes a novel encryption technique for providing security to image. We have proposed an algorithm which is based on suitable encryption method.
Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption
Project Price: Contact US
ABSTRACT: Recently, more and more attention is paid to reversible data hiding (RDH) in encrypted images, since it maintains the excellent property that the original cover can be losslessly recovered after embedded data is extracted while protecting the image content’s confidentiality. All previous methods embed data by reversibly vacating room from the encrypted images, which may be subject to some errors on data extraction and/or image restoration. In this paper, we propose a novel method by reserving room before encryption with a traditional RDH algorithm, and thus it is easy for the data hider to reversibly embed data in the encrypted image. The proposed method can achieve real reversibility, that is, data extraction and image recovery are free of any error. Experiments show that this novel method can embed more than 10 times as large payloads for the same image quality as the previous methods, such as for PSNR dB.
Robust Text Detection in Natural Scene Images
Project Price: Contact US
ABSTRACT:Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extreme Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and threshold of the clustering algorithm are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with an character classifier; text candidates with high probabilities are then eliminated and finally texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition data set the f measure is over 76% and is significantly better than the state-of-the-art performance of 71%. Experimental results on a publicly available multilingual data set also show that our proposed method can outperform the other competitive method with the f measure increase of over 9 percent. Finally, we have setup an online demo of our proposed scene text detection system.
Neuro-Fuzzy approach To Video
transmission over ZigBee
Project Price: Contact US
ABSTRACT:This research paper presents Neuro-Fuzzy applications to Moving Picture Expert Group (MPEG-4) video transmission in wireless. It can operate within 2.4 GHz frequency with a data rate of 250 kb/s, which may interfere with other wireless devices functioning within the same frequency band such as Bluetooth. MPEG-4 Variable Bit Rate (VBR) video demands large bandwidth, and may cause data loss and time delay in the data rate limited as a result of high variation in bit rate. Consequently, it is almost impracticable for MPEG-4 VBR video to be transmitted. Video can be split into frame by frame and the frame can be compressed using Jpeg Encoder it will compress and transmitted in the wifi. This paper introduces two new Neuro-Fuzzy schemes to monitor the input and the output of a data storage entitled traffic-regulating buffer. The input of the buffer is controlled by a Neuro-Fuzzy scheme to ensure that the traffic-regulating buffer neither flooded nor starved with video data. The output of the traffic-regulating buffer is observed by a second Neuro-Fuzzy scheme to make sure the departure-rate conforms to the traffic condition of wifi router. The simulation results demonstrate that the proposed two Neuro-Fuzzy schemes reduce the excessive data loss and improve the picture quality, as compared with the conventional MPEG-4 VBR video over wireless.EAACK—A Secure Intrusion-Detection System for MANETs
Project Price: Contact US
The migration to wireless network from wired network has been a global trend in the past few decades. The mobility and scalability brought by wireless network made it possible in many applications. Among all the contemporary wireless networks, Mobile Ad hoc NETwork (MANET) is one of the most important and unique applications. On the contrary to traditional network architecture, MANET does not require a fixed network infrastructure; every single node works as both a transmitter and a receiver. Nodes communicate directly with each other when they are both within the same communication range. Otherwise, they rely on their neighbors to relay messages. The self-configuring ability of nodes in MANET made it popular among critical mission applications like military use or emergency recovery. However, the open medium and wide distribution of nodes make MANET vulnerable to malicious attackers. In this case, it is crucial to develop efficient intrusion-detection mechanisms to protect MANET from attacks. With the improvements of the technology and cut in hardware costs, we are witnessing a current trend of expanding MANETs into industrial applications. To adjust to such trend, we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new intrusion-detection system named Enhanced Adaptive ACKnowledgment (EAACK) specially designed for MANETs. Compared to contemporary approaches, EAACK demonstrates higher malicious- behavior-detection rates in certain circumstances while does not greatly affect the network performances.
No comments:
Post a Comment