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The popular social networking website Facebook exposes a "public view" of user profiles to search engines which includes eight of the user's friendship links. We examine what interesting properties of the complete social graph can be inferred from this public view. In experiments on real social network data, we were able to accurately approximate the degree and centrality of nodes, compute small dominating sets, find short paths between users, and detect community structure. This work demonstrates that it is difficult to safely reveal limited information about a social network.
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Download an archive of your data from: +1s, Buzz, Contacts and Circles, Picasa Web Albums, Profile, Stream, Voice
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Sensor maakt contactloos bedienen mobiele apparaten mogelijk | Electronics | Tweakers.net Nieuws
Murata Manufacturing heeft een sensor ontwikkeld waarmee mobiele apparaten zonder aanraking kunnen worden bediend. Daarbij maakt het niet uit of een gebruiker handschoenen of natte handen heeft, claimt de elektronicafabrikant.
De sensor van Murata heeft een diagonaal van 5 millimeter. Het meetinstrument is volgens Murata vooral functioneel voor draagbare apparaten als tablets en smartphones. "Bij smartphones zet de sensor het scherm uit als de gebruiker aan het telefoneren is. Daarvoor moeten alleen de bestaande afstands- en lichtsensors vervangen worden." -
Privacy-enhanced public view for social graphs
We consider the problem of releasing a limited public view of a sensitive graph which reveals at least k edges per node. We are motivated by Facebook's public search listings, which expose user profiles to search engines along with a fixed number of each user's friends. If this public view is produced by uniform random sampling, an adversary can accurately approximate many sensitive features of the original graph, including the degree of individual nodes. We propose several schemes to produce public views which hide degree information. We demonstrate the practicality of our schemes using real data and show that it is possible to mitigate inference of degree while still providing useful public views.
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The popular social networking website Facebook exposes a "public view" of user profiles to search engines which includes eight of the user's friendship links. We examine what interesting properties of the complete social graph can be inferred from this public view. In experiments on real social network data, we were able to accurately approximate the degree and centrality of nodes, compute small dominating sets, find short paths between users, and detect community structure. This work demonstrates that it is difficult to safely reveal limited information about a social network.
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We have conducted the first thorough analysis of the market for privacy practices and policies in online social networks. From an evaluation of 45 social networking sites using 260 criteria we find that many popular assumptions regarding privacy and social networking need to be revisited when considering the entire ecosystem instead of only a handful of well-known sites. Contrary to the common perception of an oligopolistic market, we find evidence of vigorous competition for new users.
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IEEE Xplore - Prying Data out of a Social Network
Preventing adversaries from compiling significant amounts of user data is a major challenge for social network operators. We examine the difficulty of collecting profile and graph information from the popular social networking Website Facebook and report two major findings. First, we describe several novel ways in which data can be extracted by third parties. Second, we demonstrate the efficiency of these methods on crawled data. Our findings highlight how the current protection of personal data is inconsistent with user's expectations of privacy.
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DAYTUM WAS CONCEIVED BY RYAN CASE AND NICHOLAS FELTON AS AN ELEGANT AND INTUITIVE TOOL FOR COUNTING AND COMMUNICATING PERSONAL STATISTICS.
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Six Easy Ways to Graph Your Life
Your habits, behaviors, and the things you consume every day create patterns over time that say a whole lot about you as a person. It's time to graph your life.
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Jouw personal graph | Lifehacking
In het vorige artikel ‘Jouw data zijn vogelvrij’ staat beschreven dat iedereen in principe over een personal graph beschikt. Vanuit bestaande graphs zoals de link, social en interest graph kan een personal graph worden opgebouwd. In dit artikel zal Sebastian Hagens nader toelichten wat een personal graph precies inhoudt, welke data het kan bevatten, welke belangen je ermee kunt behartigen en hoe je nu al kunt beginnen met het aanleggen van je eigen personal graph. Vervolgens biedt hij een visie op de plek van persoonlijke data binnen volgende generaties internet.
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Twee indrukwekkende grafieken die je goed doen nadenken welke informatie je online deelt! Vond deze grafiek bij Forbes die rapporteren over een onderzoek van Reppler bij 300 werkgevers. Eerst en vooral welke informatie zorgt er voor dat je de job niet krijgt:
All items 10/11/2011
