While in-processing fairness approaches show promise in mitigating biased predictions, their potential impact on privacy leakage remains under-explored. We aim to address this gap by assessing the privacy risks of fairness-enhanced binary classifiers via membership inference attacks (MIAs) and attribute inference attacks (AIAs). Surprisingly, our results reveal that enhancing fairness does not necessarily lead to privacy compromises. For example, these fairness interventions exhibit increased resilience against MIAs and AIAs. This is because fairness interventions tend to remove sensitive information among extracted features and reduce confidence scores for the majority of training data for fairer predictions. However, during the evaluations, we uncover a potential threat mechanism that exploits prediction discrepancies between fair and biased models, leading to advanced attack results for both MIAs and AIAs. This mechanism reveals potent vulnerabilities of fair models and poses significant privacy risks of current fairness methods. Extensive experiments across multiple datasets, attack methods, and representative fairness approaches confirm our findings and demonstrate the efficacy of the uncovered mechanism. Our study exposes the under-explored privacy threats in fairness studies, advocating for thorough evaluations of potential security vulnerabilities before model deployments.

Federated learning is a distributed learning paradigm where a global model is trained using data samples from multiple clients but without the necessity of sharing raw data samples. However, it comes with several significant challenges in system designs, data quality, and communications. Recent research highlights a significant concern related to data privacy leakage through reserve-engineering model gradients at a malicious server. Moreover, a global model cannot provide good utility performance for individual clients when the local training data is heterogeneous in terms of quantity, quality, and distribution. Hence, personalized federated learning is highly desirable in practice to tailor the trained model for local usage. In this paper, we propose PPFed, a unified federated learning framework to simultaneously address privacy preservation and personalization. The intuition of our framework is to learn part of the model gradients at the server and the rest of the gradients at the local clients. To evaluate the effectiveness of the proposed framework, we conduct extensive experiments across four image classification datasets to show that our framework yields better privacy and personalization performance compared to the existing methods. We also claim that privacy preservation and personalization are essentially two facets of deep learning models, offering a unique perspective on their intrinsic interrelation.

Recent research has discovered that deep learning models are vulnerable to membership inference attacks, which can reveal whether a sample is in the training dataset of the victim model or not. Most membership inference attacks rely on confidence scores from the victim model for the attack purpose. However, a few studies indicate that prediction labels of the victim model's output are sufficient for launching successful attacks. Besides the well-studied classification models, segmentation models are also vulnerable to this type of attack. In this article, for the first time, we propose the label-only membership inference attacks against semantic segmentation models. With a well-designed framework of the attacks, we can achieve a considerably higher successful attacking rate compared to previous work. In addition, we have discussed several possible defense mechanisms to counter such a threat.