Automatic medical image segmentation plays a crucial role in computer-aided diagnosis. However, fully supervised learning approaches often require extensive and labor-intensive annotation efforts. To address this challenge, weakly supervised learning methods, particularly those using extreme points as supervisory signals, have the potential to offer an effective solution. In this paper, we introduce Deep Extreme Point Tracing (DEPT) integrated with a Feature-Guided Extreme Point Masking (FGEPM) algorithm for ultrasound image segmentation. Notably, our method generates pseudo labels by identifying the lowest-cost path that connects all extreme points on the feature map-based cost matrix. Additionally, an iterative training strategy is proposed to refine pseudo labels progressively, enabling continuous network improvement. Experimental results on two public datasets demonstrate the effectiveness of our proposed method. The performance of our method approaches that of the fully supervised method and outperforms several existing weakly supervised methods.

Differential privacy (DP) has been proven to be an effective universal solution for privacy protection in language models. Nevertheless, the introduction of DP incurs significant computational overhead. One promising approach to this challenge is to integrate Parameter Efficient Fine-Tuning (PEFT) with DP, leveraging the memory-efficient characteristics of PEFT to reduce the substantial memory consumption of DP. Given that fine-tuning aims to quickly adapt pretrained models to downstream tasks, it is crucial to balance privacy protection with model utility to avoid excessive performance compromise. In this paper, we propose a Relevance-based Adaptive Private Fine-Tuning (Rap-FT) framework, the first approach designed to mitigate model utility loss caused by DP perturbations in the PEFT context, and to achieve a balance between differential privacy and model utility. Specifically, we introduce an enhanced layer-wise relevance propagation process to analyze the relevance of trainable parameters, which can be adapted to the three major categories of PEFT methods. Based on the relevance map generated, we partition the parameter space dimensionally, and develop an adaptive gradient perturbation strategy that adjusts the noise addition to mitigate the adverse impacts of perturbations. Extensive experimental evaluations are conducted to demonstrate that our Rap-FT framework can improve the utility of the fine-tuned model compared to the baseline differentially private fine-tuning methods, while maintaining a comparable level of privacy protection.

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.

Membership inference attack (MIA) has been proved to pose a serious threat to federated learning (FL). However, most of the existing membership inference attacks against FL rely on the specific attack models built from the target model behaviors, which make the attacks costly and complicated. In addition, directly adopting the inference attacks that are originally designed for machine learning models into the federated scenarios can lead to poor performance. We propose GBMIA, an attack model-free membership inference method based on gradient. We take full advantage of the federated learning process by observing the target model's behaviors after gradient ascent tuning. And we combine prediction correctness and the gradient norm-based metric for membership inference. The proposed GBMIA can be conducted by both global and local attackers. We conduct experimental evaluations on three real-world datasets to demonstrate that GBMIA can achieve a high attack accuracy. We further apply the arbitration mechanism to increase the effectiveness of GBMIA which can lead to an attack accuracy close to 1 on all three datasets. We also conduct experiments to substantiate that clients going offline and the overlap of clients' training sets have great effect on the membership leakage in FL.

Federated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.

The advances of Internet technology has resulted in the rapid and pervasive development of e-commerce, which has not only changed the production and operation mode of many enterprises, but also affected the economic development mode of the whole society. This trend has incurred a strong need to store and process large amounts of sensitive data. The traditional data storage and search solutions cannot meet such requirements. To tackle this problem, in this article, we proposed Consortium Blockchain-based Distributed Secure Search (CBDSS) Scheme over encrypted data in e-Commerce environment. By integrating the blockchain and searchable encryption model, sensitive data can be effectively protected. The consortium blockchain can ensure that only authorized nodes can join the system. To fairly assign nodes for the search tasks, we developed an endorsement strategy in which two agent roles are set up to divide and match the search tasks with the virtual resources according to the load capacity of each node. The security analysis and experiments are conducted to evaluate the performance of our proposed scheme. The evaluation results have proved the reliability and security of our scheme over existing methods.