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      <div class="TdmEntry">Research Program<ul><li><a href="uid5.html&#10;&#9;&#9;  ">Probability and information theory</a></li><li><a href="uid6.html&#10;&#9;&#9;  ">Expressiveness of Concurrent Formalisms</a></li><li><a href="uid7.html&#10;&#9;&#9;  ">Concurrent constraint programming</a></li><li><a href="uid10.html&#10;&#9;&#9;  ">Model checking</a></li></ul></div>
      <div class="TdmEntry">Application Domains<ul><li><a href="uid12.html&#10;&#9;&#9;  ">Security and privacy</a></li></ul></div>
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	    Raweb 
	    2018</a> | <a href="http://www.inria.fr/en/teams/comete">Presentation of the Project-Team COMETE</a> | <a href="http://www.lix.polytechnique.fr/comete/">COMETE Web Site
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        <h2>Section: 
      Partnerships and Cooperations</h2>
        <h3 class="titre3">National Initiatives</h3>
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        <h4 class="titre4">REPAS</h4>
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            <p class="notaparagraph"><a name="uid55"> </a>Program: ANR Blanc</p>
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            <p class="notaparagraph"><a name="uid56"> </a>Project title: Reliable and Privacy-Aware Software Systems via Bisimulation Metrics</p>
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            <p class="notaparagraph"><a name="uid57"> </a>Duration: October 2016 - September 2021</p>
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            <p class="notaparagraph"><a name="uid58"> </a>Coordinator: Catuscia Palamidessi, Inria Saclay, EPI Comète</p>
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            <p class="notaparagraph"><a name="uid59"> </a>Other PI's and partner institutions: Ugo del Lago, Inria Sophia Antipolis (EPI Focus) and University of Bologna (Italy).
Vincent Danos, ENS Paris. Filippo Bonchi, ENS Lyon.</p>
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            <p class="notaparagraph"><a name="uid60"> </a>Abstract: In this project we investigate quantitative notions and tools for proving program correctness and protecting privacy. In particular, we focus on bisimulation metrics, which are the natural extension of bisimulation on quantitative systems. As a key application, we will develop a mechanism to protect the privacy of users when their location traces are collected.</p>
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        <h4 class="titre4">MAGIC</h4>
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            <p class="notaparagraph"><a name="uid61"> </a>Program: PEPS I3A</p>
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            <p class="notaparagraph"><a name="uid62"> </a>Project title: Machine Games for Information Protection</p>
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            <p class="notaparagraph"><a name="uid63"> </a>Duration: February 2018 - December 2018</p>
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            <p class="notaparagraph"><a name="uid64"> </a>Coordinator: Konstantinos Chatzikokolakis, CNRS (EPI Comète) and Ecole Polytechnique</p>
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            <p class="notaparagraph"><a name="uid65"> </a>Other PI's and partner institutions: Giovanni Cherubin, EPFL, Switzerland. Serge Haddad, ENS Cachan.</p>
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            <p class="notaparagraph"><a name="uid66"> </a>Abstract: In this project, we study a Machine Learning approach to develop methods for the Protection of Private Information. The idea is based on the Generative Adversarial Network (GAN) paradigm: the defender and the attacker are modeled as two adversaries in a game, where the payoff is the attacker’s acquisition of the user’s private data by exploiting the system vulnerabilities, side information, and probabilistic inference.</p>
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