59,965 research outputs found

    Secure Identification in Social Wireless Networks

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    The applications based on social networking have brought revolution towards social life and are continuously gaining popularity among the Internet users. Due to the advanced computational resources offered by the innovative hardware and nominal subscriber charges of network operators, most of the online social networks are transforming into the mobile domain by offering exciting applications and games exclusively designed for users on the go. Moreover, the mobile devices are considered more personal as compared to their desktop rivals, so there is a tendency among the mobile users to store sensitive data like contacts, passwords, bank account details, updated calendar entries with key dates and personal notes on their devices. The Project Social Wireless Network Secure Identification (SWIN) is carried out at Swedish Institute of Computer Science (SICS) to explore the practicality of providing the secure mobile social networking portal with advanced security features to tackle potential security threats by extending the existing methods with more innovative security technologies. In addition to the extensive background study and the determination of marketable use-cases with their corresponding security requirements, this thesis proposes a secure identification design to satisfy the security dimensions for both online and offline peers. We have implemented an initial prototype using PHP Socket and OpenSSL library to simulate the secure identification procedure based on the proposed design. The design is in compliance with 3GPP‟s Generic Authentication Architecture (GAA) and our implementation has demonstrated the flexibility of the solution to be applied independently for the applications requiring secure identification. Finally, the thesis provides strong foundation for the advanced implementation on mobile platform in future

    Secure Mobile Social Networks using USIM in a Closed Environment

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    Online social networking and corresponding mobile based applications are gaining popularity and now considered a well-integrated service within mobile devices. Basic security mechanisms normally based on passwords for the authentication of social-network users are widely deployed and poses a threat for the user security. In particular, for dedicated social groups with high confidentiality and privacy demands, stronger and user friendly principles for the authentication and identification of group members are needed. On the other hand, most of the mobile units already provide strong authentication procedures through the USIM/ISIM module. This paper explores how to build an architectural framework for secure enrollment and identification of group members in dedicated closed social groups using the USIM/SIM authentication and in particular, the 3GPP Generic Authentication Architecture (GAA), which is built upon the USIM/SIM capabilities. One part of the research is to identify the marketable use-cases with corresponding security challenges to fulfill the requirements that extend beyond the online connectivity. This paper proposes a secure identification design to satisfy the security dimensions for both online and offline peers. We have also implemented an initial proof of the concept prototype to simulate the secure identification procedure based on the proposed design. Our implementation has demonstrated the flexibility of the solution to be applied independently for applications requiring secure identification

    D.2.1.2 First integrated Grid infrastructure

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    BlogForever D2.6: Data Extraction Methodology

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    This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
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