Archive for March 31st, 2009

Mar 31, '09

Facebook has been serving up public listings for over a year now. Unlike most of the site, anybody can view public listings, even non-members.¬†They offer a window into the Facebook world for those who haven’t joined yet, since Facebook doesn’t allow full profiles to be publicly viewable by non-members (unlike MySpace and others). Of course, this window into Facebook comes with a prominent “Sign Up” button, growth still being the main mark of success in the social networking world. The goal is for non-members to stumble across a public listing, see how many friends are already using Facebook, and then join. Economists call this a network effect, and Facebook is shrewdly harnessing it.

Of course, to do this, Facebook is making public every user’s name, photo, and 8 friendship links. Affiliations with organizations, causes, or products are also listed, I just don’t have any on my profile (though my sister does). This is quite a bit of information given away by a feature many active Facebook user are unaware of. Indeed, it’s more information than the Facebook’s own privacy policy indicates is given away. When the feature was launched in 2007, every over-18 user was automatically opted-in, as have been new users since then. You can opt out, but few people do-out of more than 500 friends of mine, only 3 had taken the time to opt out. It doesn’t help that most users are unaware of the feature, since registered users don’t encounter it.

Making matters worse, public listings aren’t protected from crawling. In fact they are designed to be indexed by search engines. In our own experiments, we were able to download over 250,000 public listings per day using a desktop PC and a fairly crude Python script. For a serious data aggregator getting every user’s listing is no sweat. So what can one do with 200 million public listings?

I explored this question along with Jonathan Anderson, Frank Stajano, and Ross Anderson in a new paper which we presented today at the ACM Social Network Systems Workshop in Nuremberg. Facebook’s public listings give us a random sample of the social graph, leading to some interesting exercises in graph theory. As we describe in the paper, it turns out that this sampled graph allows us to approximate many properties of the complete network surprisingly well: degree and centrality of nodes, small dominating sets, short paths, and community structure. These are all things marketers and sociologists alike would love to know for the complete Facebook graph.

This result leads to two interesting conclusions. First, protecting a social graph is hard. Consistent with previous results, we found that giving away a seemingly small amount can allow much information to be inferred. It’s also been shown that anonymising a social graph is almost impossible.

Second, Facebook is developing a track record of releasing features and then being surprised by the privacy implications, from Beacon to NewsFeed and now Public Search. Analogous to security-critical software, where new code is extensively tested and evaluated before being deployed, social networks should have a formal privacy review of all new features before they are rolled out (as, indeed, should other web services which collect personal information).¬† Features like public search listings shouldn’t make it off the drawing board.


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