On-line sharing of images has become a key enabler of users' connectivity. Various types of images are shared through social media to represent users' interests and experiences. While extremely convenient and socially valuable, this level of pervasiveness introduces acute privacy concerns. First, once shared images may go anywhere, as copying / resharing images is straightforward. Second, the information disclosed through an image reveals aspects of users' private lives, affecting both the owner and other subjects in the image. Malicious attackers can take advantage of these unnecessary leaks to launch context-aware attacks (e.g., spearfishing) or even impersonation attacks. This project is developing methods to help users appropriately control access to their shared images.
The investigators are developing new techniques to tackle image privacy based on the image content as well as images and users' meta-data, by (a) inferring the sensitivity of a given image based on the visual properties of the images and the users' image sharing patterns, and then automatically applying the appropriate privacy settings for that image, and (b) by using discovered users' sharing patters to define access policies according to the locally enforceable controls on the domain of interest. Beyond the technical results, this project is developing a framework for understanding the common classes and categories of images with respect to the users' understanding of privacy. This framework will enable an understanding of the images that need protection and help improve user awareness of classes of images that are often unintentionally or accidentally under-protected.
|Effective start/end date
|10/1/14 → 9/30/18
- National Science Foundation: $254,618.00