LAUSR

With the explosion of multimedia data, machine learning applications are proliferating. Privacy concerns have recently resurfaced, due to a few prominent incidents of user data leakage, which divulged sensitive information to other people. Multimedia data platform providers––social networks, their affiliates or governments with access to users’ content––use algorithms to profile users by extracting or inferring demographic information, personality traits, relationships, opinions, and beliefs. These results, in turn, feed algorithms for targeted advertising but also for personalized content recommendation to maximize user engagement. While this is ostensibly for users’ benefit, they have serious privacy implications. We highlight this new problem of privacy protection against machines in contrast to the traditional problem of privacy protection against humans. We briefly touch upon our initial solution, a human-sensitivity-aware image perturbation model, which is able to modify the computational classification results of sensitive attributes while preserving the remaining attributes. We then point to many exciting open problems in this new area.