Toward Automated Multiparty Privacy Conflict Detection
Haoti Zhong, Anna Squicciarini, David J. Miller · 2018
In an effort to support users' decision making process in regards to shared and co-managed online images, in this paper we present a novel model to early detect images which may be subject to possible conflicting access control decisions. We present a group-based stochastic model able to identify potential privacy conflicts among multiple stakeholders of an image. We discuss experiments on a dataset of over 3000 online images, and compare our results with several baselines. Our approach outperforms all baselines, even the strong ones based on a Convolutional Neural Network architecture.