Privacy continues to be an important topic surrounding social media systems. A big part of the problem is that virtually all of us have a difficult time thinking about what information about us is exposed and to whom and for how long. As UMBC colleague Zeynep Tufekci points out, our intuitions in such matters come from experiences in the physical world, a place whose physics differs considerably from the cyber world.

Bruce Schneier offered a taxonomy of social networking data in a short article in the July/August issue of the IEEE Security & Privacy. A version of the article, A Taxonomy of Social Networking Data, is available on his site.

“Below is my taxonomy of social networking data, which I first presented at the Internet Governance Forum meeting last November, and again — revised — at an OECD workshop on the role of Internet intermediaries in June.

• Service data is the data you give to a social networking site in order to use it. Such data might include your legal name, your age, and your credit-card number.
• Disclosed data is what you post on your own pages: blog entries, photographs, messages, comments, and so on.
• Entrusted data is what you post on other people’s pages. It’s basically the same stuff as disclosed data, but the difference is that you don’t have control over the data once you post it — another user does.
• Incidental data is what other people post about you: a paragraph about you that someone else writes, a picture of you that someone else takes and posts. Again, it’s basically the same stuff as disclosed data, but the difference is that you don’t have control over it, and you didn’t create it in the first place.
• Behavioral data is data the site collects about your habits by recording what you do and who you do it with. It might include games you play, topics you write about, news articles you access (and what that says about your political leanings), and so on.
• Derived data is data about you that is derived from all the other data. For example, if 80 percent of your friends self-identify as gay, you’re likely gay yourself.”

I think most of us understand the first two categories and can easily choose or specify a privacy policy to control access to information in them. The rest however, are more difficult to think about and can lead to a lot of confusion when people are setting up their privacy preferences.

As an example, I saw some nice work at the 2010 IEEE International Symposium on Policies for Distributed Systems and Networks on “Collaborative Privacy Policy Authoring in a Social Networking Context” by Ryan Wishart et al. from Imperial college that addressed the problem of incidental data in Facebook. For example, if I post a picture and tag others in it, each of the tagged people can contribute additional policy constraints that can narrow access to it.

Lorrie Cranor gave an invited talk at the workshop on Building a Better Privacy Policy and made the point that even P3P privacy policies are difficult for people to comprehend.

Having a simple ontology for social media data could help us move forward toward better privacy controls for online social media systems. I like Schneier’s broad categories and wonder what a more complete treatment defined using Semantic Web languages might be like.

So, some of you may have noticed that the new myUMBC is NOW LIVE!!

We're pretty freakin' excited about it, and have received a lot of positive feedback.  The most negative thing we've heard a few times is that the yellow background is causing severe abdominal discomfort. We're looking into it.

The launch went pretty smoothly, so all we have to report today are a few simple bug fixes that folks stumbled upon:

• Fixed a typo in the Dashboard.
• Updated the list of your upcoming spotlights to be a little more intuitive. We also made it so group members who can post spotlights can see all upcoming spotlights in a group so they don't post a duplicate.
• Group categories should now be copied on content.
• Tags should now be copied on spotlights.
• Prevented Graduate Applicant and Admit from being incorrectly selected after editing existing content.
• Added a Print This Page button to pages that load inside the myUMBC frame.
• Fixed audience bug that was affecting the iPhone app.
• Added some more "page moved" links for services.

The number of citations a paper receives is generally thought to be a good and relatively objective measure of its significance and impact.

Researchers naturally are interested in knowing how to attract more citations to their papers. Publishing the results of good work helps of course, but everyone knows there are many other factors. Nature news reports on research by Gregory Webster that analyzed the 53,894 articles and review articles published in Science between 1901 and 2000.

The advice the study supports is “cite and you shall be cited”.

A long reference list at the end of a research paper may be the key to ensuring that it is well cited, according to an analysis of 100 years’ worth of papers published in the journal Science.
     The research suggests that scientists who reference the work of their peers are more likely to find their own work referenced in turn, and the effect is on the rise, with a single extra reference in an article now producing, on average, a whole additional citation for the referencing paper.
     ’There is a ridiculously strong relationship between the number of citations a paper receives and its number of references,” Gregory Webster, the psychologist at the University of Florida in Gainesville who conducted the research, told Nature. “If you want to get more cited, the answer could be to cite more people.’
…
A plot of the number of references listed in each article against the number of citations it eventually received reveal that almost half of the variation in citation rates among the Science papers can be attributed to the number of references that they include. And — contrary to what people might predict — the relationship is not driven by review articles, which could be expected, on average, to be heavier on references and to garner more citations than standard papers.

This image was created with a cool site called Wordle, where you can type in in whatever words you want, and the site will arrange your words in different patterns, colors, and fonts. You can also select these options to your preference too!

Using a combination of mathematical tricks, good programming and 35 CPU-years on Google’s servers, a group of researchers have proved that every position of Rubik’s Cube can be solved in 20 moves or less. The group consists of Kent State mathematician Morley Davidson, Google engineer John Dethridge, math teacher Herbert Kociemba, and programmer Tomas Rokicki.

This is an amazing result and a testament to more than 30 years of work on the problem. The Cube was invented in 1974 and almost immediately the subject for programs to solve it. In 1981, Morwen Thistlethwaite proved that any configuration could be solved in no more than 52 moves. Periodically, tighter upper bounds for the maximum solution length were found. This result ends the quest — there are some configurations (about 300M) that require 20 moves to solve and there are none that require more than 20 moves.

In their own words, here’s how the group solved all 43,252,003,274,489,856,000 Cube positions:

• We partitioned the positions into 2,217,093,120 sets of 19,508,428,800 positions each.
• We reduced the count of sets we needed to solve to 55,882,296 using symmetry and set covering.
• We did not find optimal solutions to each position, but instead only solutions of length 20 or less.
• We wrote a program that solved a single set in about 20 seconds.
• We used about 35 CPU years to find solutions to all of the positions in each of the 55,882,296 sets.

This reminds me of the first program I wrote for my own enjoyment, which used brute force to find all solutions to Piet Hein’s Soma Cube. In 1969 I had a summer job as the night operator for an IBM 360 and I would turn off the clock to run my program so that the management wouldn’t know how much computer time I was consuming.

Here is an Java applet from the group showing one of the hardest positions to solve.

See this BBC story more more information on this amazing result.