  People increasingly share personal information, including their photos and
photo collections, on social media. This information, however, can compromise
individual privacy, particularly as social media platforms use it to infer
detailed models of user behavior, including tracking their location. We
consider the specific issue of location privacy as potentially revealed by
posting photo collections, which facilitate accurate geolocation with the help
of deep learning methods even in the absence of geotags. One means to limit
associated inadvertent geolocation privacy disclosure is by carefully pruning
select photos from photo collections before these are posted publicly. We study
this problem formally as a combinatorial optimization problem in the context of
geolocation prediction facilitated by deep learning. We first demonstrate the
complexity both by showing that a natural greedy algorithm can be arbitrarily
bad and by proving that the problem is NP-Hard. We then exhibit an important
tractable special case, as well as a more general approach based on
mixed-integer linear programming. Through extensive experiments on real photo
collections, we demonstrate that our approaches are indeed highly effective at
preserving geolocation privacy.
