Riding with the Stars: Passenger Privacy in the NYC Taxicab Dataset

In my previous post, Differential Privacy: The Basics, I provided an introduction to differential privacy by exploring its definition and discussing its relevance in the broader context of public data release. In this post, I shall demonstrate how easily privacy can be breached and then counter this by showing how differential privacy can protect against this attack. I will also present a few other examples of differentially private queries.

The Data

There has been a lot of online comment recently about a dataset released by the New York City Taxi and Limousine Commission. It contains details about every taxi ride (yellow cabs) in New York in 2013, including the pickup and drop off times, locations, fare and tip amounts, as well as anonymized (hashed) versions of the taxi’s license and medallion numbers. It was obtained via a FOIL (Freedom of Information Law) request earlier this year and has been making waves in the…

View original post 2,314 more words

Advertisements

The 25 Meanings behind Favoriting on Twitter

I found an interesting paper describing the 25 possible motivations for clicking the “Favorite” button on someone’s tweet:

More than Liking and Bookmarking? Towards Understanding Twitter Favouriting Behaviour

The team that came up with the fav button probably did not anticipate all of these uses. I’m guessing they probably thought of 3 or 4 at most. This is interesting because it stresses the importance of studying user behavior and motivation. Changes to the behavior of the fav button will affect many of these motivations in unpredictable ways.

I wonder if Facebook’s “Like” button has similar connotations. I’d like to see a study comparing similar functionality across multiple social media sites.

I found this paper when reading this post on Medium, which is interesting in itself:

What’s Wrong with Twitter’s Latest Experiment with Broadcasting Favorites: It Steps over Social Signals While Looking for Technical Solutions