Master Thesis Projects

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OneSwarm is an open source BitTorrent client that adds social networking extensions. It allows for fine-grained control over existing and new social features. The goal is to enable privacy-preserving peer-to-peer data sharing through social connections and preferences.

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OneSwarm MatchMaker Bittorrent social extensions, extended Description OneSwarm is an open source client for the Bittorrent protocol with social extensions. The goal of this project is to add a mechanism for fine-grained control over existing and new social features. Bibliography ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Bandwidth Latency Connectivity

OneSwarm MatchMaker Bittorrent social extensions, extended KNOPPIX_… Ooo_3.0.1… ubuntu-8.10… * ,[object Object],Bandwidth Latency *Connectivity

Opera Unite TrustLink ,[object Object],[object Object],[object Object],[object Object],Creating a chain of trust Description Opera Unite is a framework for in-browser web applications based on HTML, CSS and JavaScript. The goal of this project is to create a web application that enables users to form a trusted overlay that can be used standalone or as a trust layer for other applications. Bibliography

Opera Unite TrustLink browser is always on! Creating a chain of trust Opera Unite A framework for in-browser web applications based on HTML, CSS and JavaScript A chain of trust: standalone application trust layer for other applications

SenseMe project cont’d Social networks gone distributed ,[object Object],[object Object],[object Object],[object Object],Description Under a previous master project called SenseMe a mobile application was created that can turn every Java-enabled mobile phone into an active social network node, loosely coupled into a novel social network. The goal of this project is to extend the mobile application into making better use of the limited resources available for mobile phones, by improving the matching between participant nodes. Bibliography

SenseMe project cont’d Social networks gone distributed People maintain profiles on their mobile phones (Facebook style) Can exchange them through Bluetooth (like contact cards) Others can find me through my friends Capacity on mobiles is valuable. Only some profiles can be kept. Keep the ones that are important for me Matching mechanism needed! source: http://www.berberoglu.gen.tr/senseme

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The Sociology of Nothingness: Challenges of Big Data Presentation at Towards the Good Society – European Perspectives Conference, Bucharest, 2013 Eugen Glăvan Abstract The recent evolution of the internet caught attention of the social researchers through increased interaction among individuals in various aspects of life mediated by electronic devices. The boundaries between digital and real life become blurred and we are witnessing the accumulation of abundant, detailed and comprehensive information about a wide range of behaviours, choices, opinions and artefacts of Internet users. Whether we are talking about images on Flickr, short texts on platform Twitter, search log files generated by Google, millions of megabytes of information form databases on which the issue is not capability of computing, but how to analyse such data methodologically. In my presentation I will describe the latest information concerns the definition and use of „Big Data”, showing the applications and theories developed so far and possible directions for use and development of digital sociology through these types of information. Keywords: big data, internet, digital sociology

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Semantically annotating and interlinking Open Data results in Linked Open Data which concisely and unambiguously describes a knowledge domain.However, the uptake of the Linked Data depends on its usefulness to non-Semantic Web experts. Failing to support data consumers understanding the added-value of Linked Data and possible exploitation opportunities could inhibit its diffusion. In this paper, we propose an interactive visual workflow for discovering and exploring Linked Open Data. We implemented the workflow considering academic library metadata and carried out a qualitative evaluation. We assessed the workflow’s potential impact on data consumers which bridges the offer as published Linked Open Data, and the demand as requests for: (i) higher quality data; and (ii) more applications that re-use data. More than 70% of the 34 test users agreed that the workflow fulfills its goal: it facilitates non-Semantic Web experts to understand the potential of Linked Open Data

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In peer-to-peer content distribution the lack of a central authority makes authentication difficult. Without authentication, adversary nodes can spoof identity and falsify messages in the overlay. This enables malicious nodes to launch man-in-the-middle or denial-of-service attacks. In this paper, we present a trust based content distribution for peer-to-peer overlay networks, which is built on the trust management scheme. The main concept is, before sending or accepting the traffic, the trust of the peer must be validated. Based on the success of data delivery and searching time, we calculate the trust index of a node. Then the aggregated trust index of the peers whose value is below the threshold value is considered as distrusted and the corresponding traffic is blocked. By simulation results we show that our proposed scheme achieves increased success ratio with reduced delay and drop.

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• Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. • While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. • To this end, we propose an approach to enable the protection of shared data associated with multiple users in OSNs. • We formulate an access control model to capture the essence of multiparty authorization requirements, along with a multiparty policy specification scheme and a policy enforcement mechanism.

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Dr.G.Anandharaj1, Dr.P.Srimanchari2 1Associate Professor and Head, Department of Computer Science Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506 2 Assistant Professor and Head, Department of Computer Applications Erode Arts and Science College (Autonomous), Erode (Dt) - 638001 ABSTRACT In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers. Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data

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