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computing meets p2p</a></li><li><a href="./uid5.html">Privacy-aware decentralized computations</a></li><li><a href="./uid6.html">Information dissemination over social networks</a></li><li><a href="./uid7.html">Computability and efficiency of distributed systems</a></li></ul></div>
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        <h2>Section: 
      Overall Objectives</h2>
        <h3 class="titre3">Scalability: Cloud
computing meets p2p</h3>
        <p>Our second objective is to move forward in the area of
<b>scalable infrastructures</b> for data intensive
applications. In this context, we focus significant efforts on
personalization systems, which represent one of the biggest
challenges addressed by most large stake holders.</p>
        <p><b>Hybrid infrastructures for personalisation.</b> So far, social
filtering techniques have mainly been implemented on centralized
architectures relying on smart heuristics to cope with an increasing
load of information. We argue however that, no matter how smart these
heuristics and how powerful the underlying machines running them, a
fully centralized approach might not be able to cope with the
exponential growth of the Internet and, even if it does, the price to
be paid might simply not be acceptable for its users (privacy,
ecological footprint, etc.).</p>
        <p>At the other end of the spectrum, lie fully decentralized systems
where the collaborative filtering system is implemented by the
machines of the users themselves. Such approaches are appealing for
both scalability and privacy reasons. With respect to scalability,
storage and computational units naturally grow with the number of
users. Furthermore, a p2p system provides an energy-friendly
environment where every user can feel responsible for the ecological
foot-print of her exploration of the Internet. With respect to
privacy, users are responsible for the management of their own
profiles. Potential privacy threats therefore do not come from a
big-brother but may still arise due to the presence of other users.</p>
        <p>We have a strong experience in devising and experimenting with such
kinds of p2p systems for various forms of personalization. More
specifically, we have shown that personalization can be effective
while maintaining a reasonable level of privacy. Nevertheless,
frequent connections/disconnections of users make such systems
difficult to maintain while addressing privacy attacks. For this
reason, we also plan to explore hybrid approaches where the social
filtering is performed by the users themselves, as in a p2p manner,
whereas the management of connections-disconnections, including
authentication, is managed through a server-based architecture. In
particular, we plan to explore the trade-off between the quality of
the personalization process, its efficiency and the privacy
guarantees.</p>
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