Biblio

Existing methods of generative adversarial network (GAN) use different criteria to distinguish between real and fake samples, such as probability [9],energy [44] energy or other losses [30]. In this paper, by employing the merits of deep metric learning, we propose a novel metric-based generative adversarial network (MBGAN), which uses the distance-criteria to distinguish between real and fake samples. Specifically, the discriminator of MBGAN adopts a triplet structure and learns a deep nonlinear transformation, which maps input samples into a new feature space. In the transformed space, the distance between real samples is minimized, while the distance between real sample and fake sample is maximized. Similar to the adversarial procedure of existing GANs, a generator is trained to produce synthesized examples, which are close to real examples, while a discriminator is trained to maximize the distance between real and fake samples to a large margin. Meanwhile, instead of using a fixed margin, we adopt a data-dependent margin [30], so that the generator could focus on improving the synthesized samples with poor quality, instead of wasting energy on well-produce samples. Our proposed method is verified on various benchmarks, such as CIFAR-10, SVHN and CelebA, and generates high-quality samples.

The prodigious amount of user-generated content continues to grow at an enormous rate. While it greatly facilitates the flow of information and ideas among people and communities, it may pose great threat to our individual privacy. In this paper, we demonstrate that the private traits of individuals can be inferred from user-generated content by using text classification techniques. Specifically, we study three private attributes on Twitter users: religion, political leaning, and marital status. The ground truth labels of the private traits can be readily collected from the Twitter bio field. Based on the tweets posted by the users and their corresponding bios, we show that text classification yields a high accuracy of identification of these personal attributes, which poses a great privacy risk on user-generated content. We further propose a constrained utility maximization framework for preserving user privacy. The goal is to maximize the utility of data when modifying the user-generated content, while degrading the prediction performance of the adversary. The KL divergence is minimized between the prior knowledge about the private attribute and the posterior probability after seeing the user-generated data. Based on this proposed framework, we investigate several specific data sanitization operations for privacy preservation: add, delete, or replace words in the tweets. We derive the exact transformation of the data under each operation. The experiments demonstrate the effectiveness of the proposed framework.