A summary of my group's work in using crowdsourcing techniques and wisdom of crowds to improve privacy and security. I talked about some techniques to improve crowdsourcing for anti-phishing, some ways of using lots of location data to infer location privacy preferences, and some of our early work on using crowdsourcing to understand privacy preferences regarding smartphone apps.
Entropy related to location privacy Fewer concerns in “public” places
What this means is, just looking at very obvious properties of the co-locations histories doesn't really tell you very much. Also, notice most of the performance boost is at low levels of recall. so if you want to build a high-precision classifier this is the best approach. Really there are two stories here. first it's that the intensity features do not really provide much of a gain over just looking at the number of locations, especially at high recall levels. Second, is that location based features significantly improves performance. This validates that these are clearly good things to look at when you're analyzing this kind of data
What this means is, just looking at very obvious properties of the co-locations histories doesn't really tell you very much. Also, notice most of the performance boost is at low levels of recall. so if you want to build a high-precision classifier this is the best approach. Really there are two stories here. First it's that the intensity features (time spent co-located) do not really provide much of a gain over just looking at the number of locations, especially at high recall levels. Second, is that location based features (ie entropy) significantly improves performance. This validates that these are clearly good things to look at when you're analyzing this kind of data
Entropy related to location privacy Fewer concerns in “public” places
Compare privacy as expectations with: Flow control, informed consent, not sharing information, solitude