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href="https://www.academia.edu/60102016/Scalable_Vaccine_Distribution_in_Large_Graphs_given_Uncertain_Data"><img alt="Research paper thumbnail of Scalable Vaccine Distribution in Large Graphs given Uncertain Data" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/60102016/Scalable_Vaccine_Distribution_in_Large_Graphs_given_Uncertain_Data">Scalable Vaccine Distribution in Large Graphs given Uncertain Data</a></div><div class="wp-workCard_item"><span>Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action 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class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/60102015/Military_Surveillance_Robot_Implementation_Using_Robot_Operating_System"><img alt="Research paper thumbnail of Military Surveillance Robot Implementation Using Robot Operating System" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/60102015/Military_Surveillance_Robot_Implementation_Using_Robot_Operating_System">Military Surveillance Robot Implementation Using Robot Operating System</a></div><div class="wp-workCard_item"><span>2018 IEEE Punecon</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span 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dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=60102015]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":60102015,"title":"Military Surveillance Robot Implementation Using Robot Operating System","internal_url":"https://www.academia.edu/60102015/Military_Surveillance_Robot_Implementation_Using_Robot_Operating_System","owner_id":16443746,"coauthors_can_edit":true,"owner":{"id":16443746,"first_name":"aditya","middle_initials":null,"last_name":"prakash","page_name":"adityaprakash16","domain_name":"independent","created_at":"2014-09-11T01:01:18.209-07:00","display_name":"aditya prakash","url":"https://independent.academia.edu/adityaprakash16"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102014"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60102014/Current_and_Future_Challenges_in_Mining_Large_Networks"><img alt="Research paper thumbnail of Current and Future Challenges in Mining Large Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/73691673/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60102014/Current_and_Future_Challenges_in_Mining_Large_Networks">Current and Future Challenges in Mining Large Networks</a></div><div class="wp-workCard_item"><span>ACM SIGKDD Explorations Newsletter</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We report on the Second Workshop on Mining Networks and Graphs held at the 2015 SIAM Internationa...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We report on the Second Workshop on Mining Networks and Graphs held at the 2015 SIAM International Conference on Data Mining. This half-day workshop consisted of a keynote talk, four technical paper presentations, one demonstration, and a panel on future challenges in mining large networks. We summarize the main highlights of the workshop, including expanded written summaries of the future challenges provided by the panelists. The current and future challenges discussed at the workshop and elaborated here provide valuable guidance for future research in the field</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1e6ed81856b0fe658a587d72a5f2f7d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691673,"asset_id":60102014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691673/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102014; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60102013/Approximation_Algorithms_for_Reducing_the_Spectral_Radius_to_Control_Epidemic_Spread"><img alt="Research paper thumbnail of Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread" class="work-thumbnail" src="https://attachments.academia-assets.com/73691674/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60102013/Approximation_Algorithms_for_Reducing_the_Spectral_Radius_to_Control_Epidemic_Spread">Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread</a></div><div class="wp-workCard_item"><span>Proceedings of the 2015 SIAM International Conference on Data Mining</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c2ba67d7061d8e3e904ea7b033cb1636" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691674,"asset_id":60102013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691674/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102013; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60102012/Virus_Propagation_on_Time_Varying_Networks_Theory_and_Immunization_Algorithms"><img alt="Research paper thumbnail of Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/73691675/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60102012/Virus_Propagation_on_Time_Varying_Networks_Theory_and_Immunization_Algorithms">Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Given a contact network that changes over time (say, day vs night connectivity), and the SIS (sus...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Given a contact network that changes over time (say, day vs night connectivity), and the SIS (susceptible/infected/susceptible, flu like) virus propagation model, what can we say about its epidemic threshold? That is, can we determine when a small infection will "take-off" and create an epidemic? Consequently then, which nodes should we immunize to prevent an epidemic? This is a very real problem, since, e.g. people have different connections during the day at work, and during the night at home. Static graphs have been studied for a long time, with numerous analytical results. Time-evolving networks are so hard to analyze, that most existing works are simulation studies [5]. Specifically, our contributions in this paper are: (a) we formulate the problem by approximating it by a Non-linear Dynamical system (NLDS), (b) we derive the first closed formula for the epidemic threshold of timevarying graphs under the SIS model, and finally (c) we show the usefulness of our threshold by presenting efficient heuristics and evaluate the effectiveness of our methods on synthetic and real data like the MIT reality mining graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4c10ea256c728b4bfd6959e81b494f04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691675,"asset_id":60102012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691675/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102012; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102011"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/60102011/Efficiently_spotting_the_starting_points_of_an_epidemic_in_a_large_graph"><img alt="Research paper thumbnail of Efficiently spotting the starting points of an epidemic in a large graph" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/60102011/Efficiently_spotting_the_starting_points_of_an_epidemic_in_a_large_graph">Efficiently spotting the starting points of an epidemic in a large graph</a></div><div class="wp-workCard_item"><span>Knowledge and Information Systems</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102011"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102011"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102011; 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In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0fc459a9f9d1d3f478dc29d27f4df33d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691669,"asset_id":60101951,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691669/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60101951"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60101951"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60101951; 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This half-day workshop consisted of a keynote talk, four technical paper presentations, one demonstration, and a panel on future challenges in mining large networks. We summarize the main highlights of the workshop, including expanded written summaries of the future challenges provided by the panelists. The current and future challenges discussed at the workshop and elaborated here provide valuable guidance for future research in the field</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1e6ed81856b0fe658a587d72a5f2f7d3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691673,"asset_id":60102014,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691673/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102014"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102014"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102014; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102013"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60102013/Approximation_Algorithms_for_Reducing_the_Spectral_Radius_to_Control_Epidemic_Spread"><img alt="Research paper thumbnail of Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread" class="work-thumbnail" src="https://attachments.academia-assets.com/73691674/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60102013/Approximation_Algorithms_for_Reducing_the_Spectral_Radius_to_Control_Epidemic_Spread">Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread</a></div><div class="wp-workCard_item"><span>Proceedings of the 2015 SIAM International Conference on Data Mining</span><span>, 2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c2ba67d7061d8e3e904ea7b033cb1636" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691674,"asset_id":60102013,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691674/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102013"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102013"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102013; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60102012/Virus_Propagation_on_Time_Varying_Networks_Theory_and_Immunization_Algorithms"><img alt="Research paper thumbnail of Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/73691675/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60102012/Virus_Propagation_on_Time_Varying_Networks_Theory_and_Immunization_Algorithms">Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Given a contact network that changes over time (say, day vs night connectivity), and the SIS (sus...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Given a contact network that changes over time (say, day vs night connectivity), and the SIS (susceptible/infected/susceptible, flu like) virus propagation model, what can we say about its epidemic threshold? That is, can we determine when a small infection will "take-off" and create an epidemic? Consequently then, which nodes should we immunize to prevent an epidemic? This is a very real problem, since, e.g. people have different connections during the day at work, and during the night at home. Static graphs have been studied for a long time, with numerous analytical results. Time-evolving networks are so hard to analyze, that most existing works are simulation studies [5]. Specifically, our contributions in this paper are: (a) we formulate the problem by approximating it by a Non-linear Dynamical system (NLDS), (b) we derive the first closed formula for the epidemic threshold of timevarying graphs under the SIS model, and finally (c) we show the usefulness of our threshold by presenting efficient heuristics and evaluate the effectiveness of our methods on synthetic and real data like the MIT reality mining graphs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4c10ea256c728b4bfd6959e81b494f04" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691675,"asset_id":60102012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691675/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102012; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60102011"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/60102011/Efficiently_spotting_the_starting_points_of_an_epidemic_in_a_large_graph"><img alt="Research paper thumbnail of Efficiently spotting the starting points of an epidemic in a large graph" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/60102011/Efficiently_spotting_the_starting_points_of_an_epidemic_in_a_large_graph">Efficiently spotting the starting points of an epidemic in a large graph</a></div><div class="wp-workCard_item"><span>Knowledge and Information Systems</span><span>, 2014</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60102011"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60102011"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60102011; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=60102011]").text(description); $(".js-view-count[data-work-id=60102011]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 60102011; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='60102011']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=60102011]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":60102011,"title":"Efficiently spotting the starting points of an epidemic in a large graph","internal_url":"https://www.academia.edu/60102011/Efficiently_spotting_the_starting_points_of_an_epidemic_in_a_large_graph","owner_id":16443746,"coauthors_can_edit":true,"owner":{"id":16443746,"first_name":"aditya","middle_initials":null,"last_name":"prakash","page_name":"adityaprakash16","domain_name":"independent","created_at":"2014-09-11T01:01:18.209-07:00","display_name":"aditya prakash","url":"https://independent.academia.edu/adityaprakash16"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="60101951"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/60101951/FRAPP_a_framework_for_high_accuracy_privacy_preserving_mining"><img alt="Research paper thumbnail of FRAPP: a framework for high-accuracy privacy-preserving mining" class="work-thumbnail" src="https://attachments.academia-assets.com/73691669/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/60101951/FRAPP_a_framework_for_high_accuracy_privacy_preserving_mining">FRAPP: a framework for high-accuracy privacy-preserving mining</a></div><div class="wp-workCard_item"><span>Data Mining and Knowledge Discovery</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">To preserve client privacy in the data mining process, a variety of techniques based on random pe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently. In this paper, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, FRAPP is used to demonstrate that (a)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0fc459a9f9d1d3f478dc29d27f4df33d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":73691669,"asset_id":60101951,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/73691669/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="60101951"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="60101951"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 60101951; 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