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Links 1 through 10 of 245 by Ho John Lee tagged socialsoftware

Mudhakar Srivatsa, IBM T.J. Watson Research Center
Mike Hicks, University of Maryland
The key idea of our approach is that a user may be identified by those she meets: a contact graph identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees’ DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users

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This week at the Association for Computing Machinery's Computer and Communications Security (ACM CCS) conference in Raleigh, NC, researchers Mudhakar Srivatsa and Mike Hicks are to present "Deanonymizing mobility traces: using social networks as a side-channel" [PDF]. It's interesting how the mobility traces were matched to a contact graph and then social networks were exploited to find friendships via Facebook data and business relationships via LinkedIn.

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Companies that are delving into big data may be putting customer privacy and corporate intellectual property at risk because they haven’t thought through how their data handling practices need to change.

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How does society get better at preserving privacy online? As Lawrence Lessig pointed out in his book Code and Other Laws of Cyberspace, there are four possible mechanisms: norms, law, code, and markets.

So far, we've been pretty terrible on all counts. Take norms: our primary normative mechanism for improving privacy decisions is a kind of pious finger-wagging, especially directed at kids. "You spend too much time on those Interwebs!" And yet schools and libraries and parents use network spyware to trap every click, status update, and IM from kids, in the name of protecting them from other adults. In other words: your privacy is infinitely valuable, unless I'm violating it. (Oh, and if you do anything to get around our network surveillance, you're in deep trouble.)

What about laws? In the United States, there's a legal vogue for something called "Do Not Track": users can instruct their browsers to transmit a tag that says, "Don't collect information on my user." But there's no built-

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As part of the TREC 2011 microblog track, Twitter provided identifiers for approximately 16 million tweets sampled between January 23rd and February 8th, 2011. The corpus is designed to be a reusable, representative sample of the twittersphere - i.e. both important and spam tweets are included.

The Tweets2011 corpus is unusual in that what you get is a list of tweet identifiers, and the actual tweets are downloaded directly from Twitter, using the open-source twitter-corpus-tools. However, to obtain the lists of tweets to be downloaded (i.e. the "tweet lists"), a data usage agreement must be signed. Once signed, the agreement must be emailed back to NIST, who will provide you with a username/password to download the tweet lists (in the form of a .tar.gz file).

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Report of Data Protection Audit of Facebook Ireland Published

The Office of the Data Protection Commissioner, Ireland today 21 December 2011 published the outcome of its audit of Facebook Ireland(FB-I) which was conducted over the last three months including on-site in Facebook Irelandě°˝€™s Headquarters in Dublin. The Report is a comprehensive assessment of Facebook Irelandě°˝€™s compliance with Irish Data Protection law and by extension EU law in this area.

The report is available in 2 parts: the main body of the report, including recommendations and the appendices.

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More on the Europe v Facebook personal data requests to Facebook and EU personal data rights.

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"Facebook gives explicit numbers to the directed edges (connection going from you to your friend), about how much they think you are looking for this person. I wrote a bookmarklet that makes it easy to see this list. Although you already know who you look at most, it is eerie to see the list they have come up with—and the numbers they give. The more negative the number, the more Facebook thinks you are looking for them.

To try it out, just drag the image here up to your browser’s bookmark bar. Then go to Facebook and click the bookmarklet. More explanation below.
Note: This is really interesting, but may be embarrassing to you."

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