Social media has drastically reshaped the world that allows billions of people to engage in such interactive environments to conveniently create and share content with the public. Among them, text data (e.g., tweets, blogs) maintains the basic yet important social activities and generates a rich source of user-oriented information. While those explicit sensitive user data like credentials has been significantly protected by all means, personal private attribute (e.g., age, gender, location) disclosure due to inference attacks is somehow challenging to avoid, especially when powerful natural language processing (NLP) techniques have been effectively deployed to automate attribute inferences from implicit text data. This puts users' attribute privacy at risk. To address this challenge, in this paper, we leverage the inherent vulnerability of machine learning to adversarial attacks, and design a novel text-space Adversarial attack for Social Good, called Adv4SG. In other words, we cast the problem of protecting personal attribute privacy as an adversarial attack formulation problem over the social media text data to defend against NLP-based attribute inference attacks. More specifically, Adv4SG proceeds with a sequence of word perturbations under given constraints such that the probed attribute cannot be identified correctly. Different from the prior works, we advance Adv4SG by considering social media property, and introducing cost-effective mechanisms to expedite attribute obfuscation over text data under the black-box setting. Extensive experiments on real-world social media datasets have demonstrated that our method can effectively degrade the inference accuracy with less computational cost over different attribute settings, which substantially helps mitigate the impacts of inference attacks and thus achieve high performance in user attribute privacy protection.
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
LAAS-CNRS（LAAS-法国国家科学研究中心）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；École de Technologie Supérieure（高等技术学院）；Université du Québec à Montréal（魁北克大学蒙特利尔分校）；INSA（法国国立应用科学学院）
MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control
Huazhong University of Science and Technology（华中科技大学）；Ping An Property & Casualty Insurance Company of China, Ltd.（中国平安财产保险股份有限公司）；Beijing Institute of Technology（北京理工大学）；Brilliance Technology Co. Ltd.（华晨科技股份有限公司）
Personal Attribute Leakage in Federated Speech Models
Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed人工智能大学）；Maastricht University（马斯特里赫特大学）；University of Groningen（格罗宁根大学）；University of Edinburgh（爱丁堡大学）
