IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88Networks and network security1. A Distributed Control Law for Load Balancing in Content Delivery NetworksABSTRACTIn this paper, we face the challenging issue of defining and implementing an effective lawfor load balancing in Content Delivery Networks (CDNs). We base our proposal on a formalstudy of a CDN system, carried out through the exploitation of a fluid flow modelcharacterization of the network of servers. Starting from such characterization, wederive and prove a lemma about the network queues equilibrium. This result is thenleveraged in order to devise a novel distributed and time-continuous algorithm for loadbalancing, which is also reformulated in a time-discrete version. The discrete formulation ofthe proposed balancing law is eventually discussed in terms of its actual implementation in areal-world scenario. Finally, the overall approach is validated by means of simulations.2. TAM: A Tiered Authentication of Multicast Protocol for Ad-Hoc NetworksABSTRACTAd-hoc networks are becoming an effective tool for many mission critical applications suchas troop coordination in a combat field, situational awareness, etc. These applications arecharacterized by the hostile environment that they serve in and by the multicast-style ofcommunication traffic. Therefore, authenticating the source and ensuring the integrity of themessage traffic become a fundamental requirement for the operation and management ofthe network. However, the limited computation and communication resources, the largescale deployment and the unguaranteed connectivity to trusted authorities make known
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88 solutions for wired and single-hop wireless networks inappropriate. This paper presents a new Tiered Authentication scheme for Multicast traffic (TAM) for large scale dense ad- hoc networks. TAM combines the advantages of the time asymmetry and the secret information asymmetry paradigms and exploits network clustering to reduce overhead and ensure scalability. Multicast traffic within a cluster employs a one-way hash function chain in order to authenticate the message source. Cross-cluster multicast traffic includes message authentication codes (MACs) that are basedon a set of keys. Each cluster uses a unique subset of keys to look for its distinct combination of valid MACs in the message in order to authenticate the source. The simulation and analytical results demonstrate the performance advantage of TAM in terms of bandwidth overhead and delivery delay.3. Privacy- and Integrity-Preserving Range Queries in Sensor Networks Abstract—The architecture of two-tiered sensor networks, where storage nodes serve as an intermediate tier between sensors and a sink for storing data and processing queries, has been widely adopted because of the benefits of power and storage saving for sensors as well as the efficiency of query processing. However, the importance of storage nodes also makes them attractive to attackers. In this paper, we propose SafeQ, a protocol that prevents attackers from gaining information from both sensor collected data and sink issued queries. SafeQ also allows a sink to detect compromised storage nodes when they misbehave. To preserve privacy, SafeQ uses a novel technique to encode both data and queries such that a storage node can correctly process encoded queries over encoded data without knowing their values. To preserve integrity, we propose two schemes—one using Merkle hash trees and another using a new data structure called neighborhood chains—to generate integrity verification information so that a sink can use this information to verify whether the result of a query contains exactly the data items that satisfy the query. To improve performance, we propose an optimization technique using Bloom filters to reduce the communication cost between sensors and storage nodes.
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88Wireless Networks4. Adaptive Opportunistic Routing for Wireless Ad Hoc NetworksABSTRACTA distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks isproposed. The proposed scheme utilizes a reinforcement learning framework toopportunistically route the packets even in the absence of reliable knowledge about channelstatistics and network model. This scheme is shown to be optimal with respect to anexpected average per-packet reward criterion. The proposed routing scheme jointlyaddresses the issues of learning and routing in an opportunistic context, wherethe network structure is characterized by the transmission success probabilities. Inparticular, this learning framework leads to a stochastic routing scheme that optimally“explores” and “exploits” the opportunities in the network.5. Local Greedy Approximation for Scheduling in Multihop Wireless NetworksABSTRACTIn recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multihop wireless networks.A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is agood candidate because its empirically observed performance is close to optimal in a varietyof network settings. However, its distributed realization requires high complexity, whichbecomes a major obstacle for practical implementation. In this paper, we develop simpledistributed greedy algorithms for scheduling in multihop wireless networks. We reduce the
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulationresults show that the new algorithms approximate the performance of GMS, and outperformthe state-of-the-art distributed scheduling policies.Mobile computing6. Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc NetworksABSTRACTThis paper addresses the problem ofdelivering data packets for highly dynamic mobile ad hoc networks in areliable and timelymanner. Most existing ad hoc routing protocols are susceptible to node mobility,especially for large-scale networks. Driven by this issue, we propose an efficient Position-based Opportunistic Routing (POR) protocol which takes advantage of the stateless propertyof geographic routing and the broadcast nature of wireless medium. When a data packet issent out, some of the neighbor nodes that have overheard the transmission will serve asforwarding candidates, and take turn to forward the packet if it is not relayed by the specificbest forwarder within a certain period of time. By utilizing such in-the-air backup,communication is maintained without being interrupted. The additional latency incurred bylocal route recovery is greatly reduced and the duplicate relaying caused by packet rerouteis also decreased. In the case of communication hole, a Virtual Destination-based VoidHandling (VDVH) scheme is further proposed to work together with POR. Both theoreticalanalysis and simulation results show that POR achieves excellent performance even underhigh node mobility with acceptable overhead and the new void handling scheme also workswell.7. Distributed Throughput Maximization in Wireless Networks via RandomPower Allocation
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88ABSTRACTWe develop a distributed throughput-optimal power allocation algorithm in wirelessnetworks. The study of this problem has been limited due to the nonconvexity of theunderlying optimization problems that prohibits an efficient solution even in a centralizedsetting. By generalizing the randomization framework originally proposed for input queuedswitches to SINR rate-based interference model, we characterize the throughput-optimalityconditions that enable efficient and distributed implementation. Using gossiping algorithm,we develop a distributed power allocation algorithm that satisfies the optimality conditions,thereby achieving (nearly) 100 percent throughput. We illustrate the performance of ourpower allocation solution through numerical simulation.8. Hop-by-Hop Routing in Wireless Mesh Networks with Bandwidth GuaranteesABSTRACTWireless Mesh Network (WMN) has become an important edge network to provide Internetaccess to remote areas and wireless connections in a metropolitan scale. In this paper, westudy the problem of identifying the maximum available bandwidth path, a fundamentalissue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth,a well-known bottleneck metric in wired networks, is neither concave nor additive inwireless networks. We propose a new path weight which captures the available pathbandwidth information. We formally prove that our hop-by-hop routing protocolbased on the new path weight satisfies the consistency and loop-freeness requirements. Theconsistency property guarantees that each node makes a proper packet forwarding decision,so that a data packet does traverse over the intended path. Our extensive simulationexperiments also show that our proposed path weight outperforms existing path metrics inidentifying high-throughput paths.
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88Wireless Sensor Networks9. On the Throughput Capacity of Wireless Sensor Networks with Mobile Relays ABSTRACTIn wireless sensor networks (WSNs), it is difficult to achieve a large data collection ratebecause sensors usually have limited energy and communication resources. Such an issue isbecoming more and more challenging with the emerging of information-intensiveapplications that require high data collection rate. To address this issue, in this paper, weinvestigate the throughput capacity of WSNs where multiple mobile relays are deployed tocollect data from static sensors and forward them to a static sink. To facilitate thediscussion, we propose a new mobile relay assisted data collection (MRADC) model.Based on this model, we analyze the achievable throughput capacity of largescale WSNsusing a constructive approach, which can achieve a certain throughput by choosingappropriate mobility parameters. Our analysis illustrates that, if the number of relays is lessthan a threshold, then the throughput capacity can be increased linearly with morerelays. On the other hand, if the number is greater than the threshold, then the throughputcapacity becomes a constant, and the capacity gain over a static WSN depends on twofactors: the transmission range and the impact of interference. To verify our analysis, weconduct extensive simulation experiments, which validate the selection of mobilityparameters, and which demonstrate the same throughput behaviors obtained by analysis.Knowledge and Data Mining10. Publishing Search Logs—A Comparative Study of Privacy Guarantees
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88ABSTRACTSearch engine companies collect the “database of intentions,” the histories of their userssearch queries. These search logs are a gold mine for researchers. Search enginecompanies, however, are wary of publishing search logs in order not to disclose sensitiveinformation. In this paper, we analyze algorithms for publishing frequent keywords,queries, and clicks of a search log. We first show how methods that achieve variants of k-anonymity are vulnerable to active attacks. We then demonstrate that the strongerguarantee ensured by ε-differential privacy unfortunately does not provide any utility forthis problem. We then propose an algorithm ZEALOUS andshow how to set its parametersto achieve (ε, δ)-probabilistic privacy. We also contrast our analysis of ZEALOUS with ananalysis by Korolova et al.  that achieves (ε,δ)-indistinguishability. Our paperconcludes with a large experimental study using real applications where we compareZEALOUS and previous work that achieves k-anonymity in search log publishing. Our resultsshow that ZEALOUS yields comparable utility to k-anonymity while at the same timeachieving much stronger privacy guarantees.11. Efficient Multi-dimensional Fuzzy Search for Personal InformationManagement SystemsABSTRACTWith the explosion in the amount of semistructured data users access and store in personalinformation management systems, there is a critical need for powerful search tools toretrieve often very heterogeneous data in a simple and efficient way. Existing tools typicallysupport some IR-style ranking on the textual part of the query, but only consider structure(e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We proposea novel multidimensional search approach that allows users to perform fuzzy searches for
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88 structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most relevant files that match multidimensional queries. We perform a thorough experimental evaluation of our approach and show that our relaxation and scoring framework for fuzzy query conditions in noncontent dimensions can significantly improve ranking accuracy. We also show that our query processing strategies perform and scale well, making our fuzzy search approach practical for every day usage.12. Prediction of Users Web-Browsing Behavior: Application of Markov Model ABSTRACT Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting users behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model andall-$K$th Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all-$K$th model.
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-8813. A Probabilistic Scheme for Keyword-Based Incremental Query Construction ABSTRACT Databases enable users to precisely express their informational needs using structured queries. However, database query construction is a laborious and error-prone process, which cannot be performed well by most end users. Keyword search alleviates the usability problem at the price of query expressiveness. As keyword search algorithms do not differentiate between the possible informational needs represented by a keyword query, users may not receive adequate results. This paper presents IQ P - a novel approach to bridge the gap between usability of keyword search and expressiveness of database queries. IQP enables a user to start with an arbitrary keyword query and incrementally refine it into a structured query through an interactive interface. The enabling techniques of IQP include: 1) a probabilistic framework for incremental query construction; 2) a probabilistic model to assess the possible informational needs represented by a keyword query; 3) an algorithm to obtain the optimal query construction process. This paper presents the detailed design of IQ P, and demonstrates its effectiveness and scalability through experiments over real-world data and a user study.14. Ranking Model Adaptation for Domain-Specific Search Abstract—With the explosive emergence of vertical search domains, applying the broad- based ranking model directly to different domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for labeling data and time-consuming for training models. In this paper, we address these difficulties by proposing a regularization based algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88the amount of labeled data and the training cost is reduced while the performance is stillguaranteed. Our algorithm only requires the prediction from the existing ranking models,rather than their internal representations or the data from auxiliary domains. In addition,we assume that documents similar in the domain-specific feature space should haveconsistent rankings, and add some constraints to control the margin and slack variables ofRA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitativelyestimate if an existing ranking model can be adapted to a new domain. Experimentsperformed over Letor and two large scale datasets crawled from a commercial search enginedemonstrate the applicabilities of the proposed ranking adaptation algorithms and theranking adaptability measurement.15. Slicing: A New Approach to Privacy Preserving Data PublishingABSTRACTSeveral anonymization techniques, such as generalization and bucketization, have beendesigned for privacy preserving microdata publishing. Recent work has shown thatgeneralization loses considerable amount of information, especially for high-dimensionaldata. Bucketization, on the other hand, does not prevent membership disclosure and doesnot apply for data that do not have a clear separation between quasi- identifying attributesand sensitive attributes. In this paper, we present a novel technique called slicing, whichpartitions the data both horizontally and vertically. We show that slicing preserves betterdata utility than generalization and can be used for membership disclosure protection.Another important advantage of slicing is that it can handle high-dimensional data. We showhow slicing can be used for attribute disclosure protection and develop an efficient algorithmfor computing the sliced data that obey the ℓ-diversity requirement. Our workloadexperiments confirm that slicing preserves better utility than generalization and is more
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88effective than bucketization in workloads involving the sensitive attribute. Our experimentsalso demonstrate that slicing can be used to prevent membership disclosure.16. Data Mining Techniques for Software Effort Estimation: A Comparative StudyA predictive model is required to be accurate and comprehensible in order to inspireconfidence in a business setting. Both aspects have been assessed in a software effortestimation setting by previous studies. However, no univocal conclusion as to whichtechnique is the most suited has been reached. This study addresses this issue byreporting on the results of a large scale benchmarking study. Different types of techniquesare under consideration, including techniques inducing tree/rule-based models like M5 andCART, linear models such as various types of linear regression, nonlinear models (MARS,multilayered perceptron neural networks, radial basis function networks, and least squaressupport vector machines), and estimation techniques that do not explicitly induce a model(e.g., a case-based reasoning approach). Furthermore, the aspect of feature subsetselection by using a generic backward input selection wrapper is investigated. The resultsare subjected to rigorous statistical testing and indicate that ordinary least squaresregression in combination with a logarithmic transformation performs best. Another keyfinding is that by selecting a subset of highly predictive attributes such as project size,development, and environment related attributes, typically a significant increase inestimation accuracy can be obtained.17. Ranking and Clustering Software Cost Estimation Models through a MultipleComparisons AlgorithmSoftware Cost Estimation can be described as the process of predicting the most realisticeffort required to complete a software project. Due to the strong relationship of accurateeffort estimations with many crucial project management activities, the research communityhas been focused on the development and application of a vast variety of methods andmodels trying to improve the estimation procedure. From the diversity of methods emerged
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88 the need for comparisons to determine the best model. However, the inconsistent results brought to light significant doubts and uncertainty about the appropriateness of the comparison process in experimental studies. Overall, there exist several potential sources of bias that have to be considered in order to reinforce the confidence of experiments. In this paper, we propose a statistical framework based on a multiple comparisons algorithm in order to rank several cost estimation models, identifying those which have significant differences in accuracy and clustering them in non-overlapping groups. The proposed framework is applied in a large-scale setup of comparing 11 prediction models over 6 datasets. The results illustrate the benefits and the significant information obtained through the systematic comparison of alternative methods.18. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning Title and Guide Abstract—Multimedia educational resources play an important role in education, particularly for distance learning environments. With the rapid growth of the multimedia web, large numbers of educational video resources are increasingly being created by several different organizations. It is crucial to explore, share, reuse, and link these educational resources for better e-learning experiences. Most of the video resources are currently annotated in an isolated way, which means that they lack semantic connections. Thus, providing the facilities for annotating these video resources is highly demanded. These facilities create the semantic connections among video resources and allow their metadata to be understood globally. Adopting Linked Data technology, this paper introduces a video annotation and browser platform with two online tools: Annomation and SugarTube. Annomation enables users to semantically annotate video resources using vocabularies defined in the Linked
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88Data cloud. SugarTube allows users to browse semantically linked educational videoresources with enhanced web information from different online resources. In the prototypedevelopment, the platform uses existing video resources for the history courses from theOpen University (United Kingdom). The result of the initial development demonstrates thebenefits of applying Linked Data technology in the aspects of reusability, scalability, andextensibility.Cloud Computing19. Scalable and Secure Sharing of Personal Health Records in Cloud Computingusing Attribute-based EncryptionABSTRACTPersonal health record (PHR) is an emerging patient-centric model of health informationexchange, which is often outsourced to be stored at a third party, such as cloud providers.However, there have been wide privacy concerns as personal health information could beexposed to those third party servers and to unauthorized parties. To assure the patientscontrol over access to their own PHRs, it is a promising method to encrypt the PHRs beforeoutsourcing. Yet, issues such as risks of privacy exposure, scalability in key management,flexible access and efficient user revocation, have remained the most important challengestoward achieving fine-grained, cryptographically enforced data access control. In this paper,we propose a novel patient-centric framework and a suite of mechanisms for data accesscontrol to PHRs stored in semi-trusted servers. To achieve fine-grained and scalable dataaccess control for PHRs, we leverage attribute based encryption (ABE) techniques to encrypteach patients PHR file. Different from previous works in secure data outsourcing, wefocus on the multiple data owner scenario, and divide the users in the PHR system into
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88multiple security domains that greatly reduces the key management complexity forowners and users. A high degree of patient privacy is guaranteed simultaneously byexploiting multi-authority ABE. Our scheme also enables dynamic modification of accesspolicies or file attributes, supports efficient on-demand user/attribute revocation and break-glass access under emergency scenarios. Extensive analytical and experimental results arepresented which show the security, scalability and efficiency of our proposed scheme.20. Enabling Secure and Efficient Ranked Keyword Search over OutsourcedCloud DataCloud computing economically enables the paradigm of data service outsourcing. However,to protect dataprivacy, sensitive cloud data have to be encrypted before outsourced to thecommercial public cloud, which makes effective data utilization service a very challengingtask. Although traditional searchable encryption techniques allow users tosecurely search over encrypted data through keywords, they support onlyBooleansearch and are not yet sufficient to meet the effective data utilization need that isinherently demanded by large number of users and huge amount of data files in cloud. Inthis paper, we define and solve the problemof secureranked keyword search over encrypted cloud data. Ranked search greatlyenhances system usability by enablingsearch result relevance ranking instead of sendingundifferentiated results, and further ensures the file retrieval accuracy. Specifically, weexplore the statistical measure approach, i.e., relevance score, from information retrieval tobuild a secure searchable index, and develop a one-to-many order-preserving mappingtechnique to properly protect those sensitive score information. The resulting design is ableto facilitate efficient server-sideranking without losing keyword privacy. Thorough analysisshows that our proposed solution enjoys “as-strong-as-possible” security guaranteecompared to previous searchable encryption schemes, while correctly realizing the goalof ranked keyword search. Extensive experimental results demonstrate the efficiency of theproposed solution.
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-8821. An Efficient andSecureynamic Auditing Protocolfor Data Storage in Cloud ComputingABSTRACTIn cloud computing, data owners host their data on cloud servers and users(data consumers) can access thedata from cloud servers. Due to the data outsourcing,however, this new paradigm of data hosting service also introduces new security challenges,which requires an independent auditing service to check the data integrity inthe cloud.Some existing remote integrity checking methods can only serve for static archive data andthus cannot be applied to the auditing service since the data in the cloud can be dynamicallyupdated. Thus, an efficient and secure dynamic auditing protocol is desired toconvince data owners that the data are correctly stored in the cloud. In this paper, we firstdesign an auditing framework for cloud storage systems and propose an efficient andprivacy-preserving auditing protocol. Then, we extend our auditing protocol to supportthe data dynamic operations, which is efficient and provably secure in the random oraclemodel. We further extend our auditing protocol to support batch auditing for both multipleowners and multiple clouds, without using any trusted organizer. The analysis andsimulation results show that our proposed auditing protocols are secure and efficient,especially it reduce the computation cost of the auditor.22. Towards Secure and Dependable Storage Services in Cloud Computing.Cloud storage enables users to remotely store their data and enjoy the on-demand highquality cloud applications without the burden of local hardware and software management.Though the benefits are clear, such a service is also relinquishing users physical possessionof their outsourced data, which inevitably poses new security risks toward the correctness ofthe data in cloud. In order to address this new problem and further achieve a secure and
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88dependable cloud storage service, we propose in this paper a flexible distributed storageintegrity auditing mechanism, utilizing the homomorphic token and distributed erasure-coded data. The proposed design allows users to audit the cloud storage with verylightweight communication and computation cost. The auditing result not only ensuresstrong cloud storage correctness guarantee, but also simultaneously achieves fast data errorlocalization, i.e., the identification of misbehaving server. Considering the cloud data aredynamic in nature, the proposed design further supports secure and efficient dynamicoperations on outsourced data, including block modification, deletion, and append. Analysisshows the proposed scheme is highly efficient and resilient against Byzantine failure,malicious data modification attack, and even server colluding attacks.23. Ensuring Distributed Accountability for Data Sharing in the Cloud Abstract—Cloud computing enables highly scalable services to be easily consumed overthe Internet on an as-needed basis. A major feature of the cloud services is that users’ dataare usually processed remotely in unknown machines that users do not own or operate.While enjoying the convenience brought by this new emerging technology, users’ fears oflosing control of their own data (particularly, financial and health data) can become asignificant barrier to the wide adoption of cloud services. To address this problem, in thispaper, we propose a novel highly decentralized information accountability framework tokeep track of the actual usage of the users’ data in the cloud. In particular, we propose anobject-centered approach that enables enclosing our logging mechanism together withusers’ data and policies. We leverage the JAR programmable capabilities to both create adynamic and traveling object, and to ensure that any access to users’ data will triggerauthentication and automated logging local to the JARs. To strengthen user’s control, wealso provide distributed auditing mechanisms. We provide extensive experimental studiesthat demonstrate the efficiency and effectiveness of the proposed approaches.
IEEE 2012 Titles & Abstract FOR REGISTER: www.finalyearstudentsproject.com CONTACT NO.: 91-9176696486. Address: No.73, Karuneegar street, Adambakkam, Chennai-88 Information Forensics and Security24. A Novel Data Embedding Method Using Adaptive Pixel Pair Matching This paper proposes a new data-hiding method based on pixel pair matching (PPM). The basic idea of PPM is touse the values of pixel pair as a reference coordinate, and search a coordinate in the neighborhood set of thispixel pair according to a given message digit. The pixel pair is then replaced by the searched coordinate to conceal the digit. Exploiting modification direction (EMD) and diamond encoding (DE) are two data- hidingmethods proposed recently based on PPM. The maximum capacity of EMD is 1.161 bpp and DE extends the payload of EMD by embedding digits in a larger notational system. The proposed method offers lower distortion than DE by providing more compact neighborhood sets and allowing embedded digits in any notational system. Compared with the optimal pixel adjustment process (OPAP) method, the proposed method always has lower distortion for various payloads. Experimental results reveal that the proposed method not only provides better performance than those of OPAP and DE, but also is secure under the detection of some well-known steganalysis techniques.