Wenxin Mao, Ruiqi Wang, Jiyu Guo, Jichuan Zeng, Cuiyun Gao, Peiyi Han, Chuanyi Liu. Findings of the Association for Computational Linguistics ACL 2024. 2024.

In this paper, we study the problem of privacy-preserving data synthesis (PPDS) for tabular data in a distributed multi-party environment. In a decentralized setting, for PPDS, federated generative models with differential privacy are used by the existing methods. Unfortunately, the existing models apply only to images or text data and not to tabular data. Unlike images, tabular data usually consist of mixed data types (discrete and continuous attributes) and real-world datasets with highly imbalanced data distributions. Existing methods hardly model such scenarios due to the multimodal distributions in the decentralized continuous columns and highly imbalanced categorical attributes of the clients. To solve these problems, we propose a federated generative model for decentralized tabular data synthesis (HT-Fed-GAN). There are three important parts of HT-Fed-GAN: the federated variational Bayesian Gaussian mixture model (Fed-VB-GMM), which is designed to solve the problem of multimodal distributions; federated conditional one-hot encoding with conditional sampling for global categorical attribute representation and rebalancing; and a privacy consumption-based federated conditional GAN for privacy-preserving decentralized data modeling. The experimental results on five real-world datasets show that HT-Fed-GAN obtains the best trade-off between the data utility and privacy level. For the data utility, the tables generated by HT-Fed-GAN are the most statistically similar to the original tables and the evaluation scores show that HT-Fed-GAN outperforms the state-of-the-art model in terms of machine learning tasks.

The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets.

Browser-based cloud storage services are still broadly used in enterprises for online sharing and collaboration. However, sensitive information in images or documents may be easily leaked outside trusted enterprise on-premises due to such cloud services. Existing solutions to prevent data leakage in cloud storage services either limit many functionalities of cloud applications or are difficult to be scaled to various cloud applications. In this paper, we propose CloudDLP, a transparent and scalable approach for enterprises to automatically sanitize sensitive data in images and documents with various browser-based cloud applications. CloudDLP is deployed as an internet gateway within the premises of an enterprise using JavaScript injecting techniques and deep learning methods to sanitize sensitive premise data. It neither compromises the user experience nor significantly affects application functionalities in browserbased cloud storage services.We have evaluated CloudDLP with a number of real-world cloud applications. Our experimental results show that it can achieve automatic data sanitization with cloud storage services while preserving most functionalities of cloud applications.

Risk assessment is crucial for quantifying driving environment risks and reducing the Safety of the Intended Functionality (SOTIF) uncertainty in Autonomous Vehicles (AVs). Traditional methods, however, often concentrate on single-scenario metrics and are insufficient in capturing the complexities of multi-participant and multi-directional interactions. This letter introduces a risk assessment model capable of evaluating risks across diverse traffic participants, multiple directions, and complex multi-risk scenarios. Our model leverages state information from both the ego vehicle and surrounding traffic to compute a comprehensive risk probability distribution and environmental impact assessment. It achieves more precise risk quantification through an iterative process that calculates cumulative environmental costs and derives scalar risk values via sampling and summation within a map grid. Rigorously validated on extensive real-world datasets (inD, highD, rounD), our model demonstrates superior accuracy and scalability across diverse driving scenarios compared to traditional metrics like TTC, THW, RSS, and other field models, offering advantages in computational efficiency and parameter adjustability. The results showed that our model not only accurately quantifies environmental risks but also significantly enhances the safety and reliability of AVs, especially in high-risk, long-tail cases, making it a robust tool for the development and deployment of safer autonomous driving systems.