Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized. However, recent studies have revealed that the training phase of FL is vulnerable to reconstruction attacks, such as attribute inference attacks (AIA), where adversaries exploit exchanged messages and auxiliary public information to uncover sensitive attributes of targeted clients. While these attacks have been extensively studied in the context of classification tasks, their impact on regression tasks remains largely unexplored. In this paper, we address this gap by proposing novel model-based AIAs specifically designed for regression tasks in FL environments. Our approach considers scenarios where adversaries can either eavesdrop on exchanged messages or directly interfere with the training process. We benchmark our proposed attacks against state-of-the-art methods using real-world datasets. The results demonstrate a significant increase in reconstruction accuracy, particularly in heterogeneous client datasets, a common scenario in FL. The efficacy of our model-based AIAs makes them better candidates for empirically quantifying privacy leakage for federated regression tasks.

As the digitization of travel industry accelerates, analyzing and understanding travelers' behaviors becomes increasingly important. However, traveler data frequently exhibit high data sparsity due to the relatively low frequency of user interactions with travel providers. Compounding this effect the multiplication of devices, accounts and platforms while browsing travel products online also leads to data dispersion. To deal with these challenges, probabilistic traveler matching can be used. Most existing solutions for user matching are not suitable for traveler matching as a traveler's browsing history is typically short and URLs in the travel industry are very heterogeneous with many tokens. To deal with these challenges, we propose the similarity based multi-view information fusion to learn a better user representation from URLs by treating the URLs as multi-view data. The experimental results show that the proposed multi-view user representation learning can take advantage of the complementary information from different views, highlight the key information in URLs and perform significantly better than other representation learning solutions for the user matching task.

Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even during human inspection. Face normalization methods try to deal with such challenges by removing intra-personal variations from an input image while keeping the identity the same. However, most face normalization methods can only remove one or two variations and ignore dataset biases such as skin color bias. The outputs of many face normalization methods are also not realistic to human observers. In this work, a style based face normalization model (StyleFNM) is proposed to remove most intra-personal variations including large changes in pose, bad or harsh illumination, low resolution, blur, facial expressions, and accessories like sunglasses among others. The dataset bias is also dealt with in this paper by controlling a pretrained GAN to generate a balanced dataset of passport-like images. The experimental results show that StyleFNM can generate more realistic outputs and can improve significantly the accuracy and fairness of face recognition systems.