Hopefully most folks have seen news of the paper in the Proceedings of the National Academy of Sciences that shows how analysis of what you “Like” in Facebook can be used to infer aspects of your personality. The gist of this is that your “Likes” form a profile about you that can be associated with other aspects of your life — religious affiliation, sexual preference, drug use — with a certain probability. Presumably other visible aspects of your preferences could also be used in similar ways — who you follow on Twitter, topics in your blog (or on Twitter), etc. Combining various of these methods is likely to increase the probability that a given “assertion” is accurate.
Some of these things may be “don’t care” for you, but others could be problematic. With the tendency of employers, schools, and others to evaluate your web presence as part of their interview and other processes, this becomes another subtle channel for discrimination. Of course the results can yield “false positives”, but it is unlikely that you will know about the uses/abuses of such evaluations and as such won’t have any way to counter the conclusions.
The automated evaluation of many aspects of our digital footprints is something we need to constantly revisit. “Traffic analysis” is a concept that has been applied to determine communications patterns but also to find the recipe for trade secret foods (three tankers of corn syrup, one of vanilla, etc.) Watching where you go on the web is something your employer, ISP, search engine, and others can do (it is a key aspect of Facebook “Like” and other such options — and you don’t need to actually select “Like” for Facebook to record your trip to that site.) Some aspects of this are only visible to the data collector (Facebook) and their friends, but publicly accessible indicators such as “Likes” on your Facebook page are open to analysis by any interested parties. No doubt we will see emerging services that will either do this on request, or will sort out groups of interest for targeted advertising, or other uses.
As with many possible privacy issues, it is the aggregation of data points that starts to reveal details we might have assumed were at least obfuscated if not private.