The unauthorized acquisition of 3D assets by means of advanced techniques in 3D reconstruction is a major but often overlooked threat to publishers of videos. Preventing such threats is challenging due to the uninterpretable nature of 3D reconstruction and the diversity in requirements of 3D model demonstration. In this paper, we introduce VidGuard3D-a visual risk analysis approach that quantifies and locates the sources of risk for video-based 3D asset reconstruction attacks. Our approach uses attack simulation to support users in formulating a comprehensive understanding of 3D asset leakage risks, with a particular focus on the correlations between video segments and exposure risks of user-specified areas in the asset. We also proposed a prototype system that integrates this approach to facilitate video editing according to the knowledge of correlations. Two operations of video editing can be swiftly applied by users to form editing plans and minimize detected leakage risks. Finally, we conducted a user study and case studies that demonstrated the practicality and effectiveness of our approach.

The security of individual privacy is paramount for trajectory publication, while preserving trajectory utility is also essential to serve analysis tasks such as urban planning and transportation development. To assess and maintain trajectory utility, existing studies consider geographic context. However, innate semantic characteristics of trajectories (e.g., origin, destination, stay point, path) have been overlooked, which prevents data owners from specifying task-specific utility measurements and, consequently, from achieving a delicate balance between privacy and utility. This paper proposes an interactive trajectory publishing approach driven by flexible utility considerations, which processes trajectory points according to their semantics to fulfill diverse utility requirements. Concretely, we decouple trajectories into origin-destination (OD) and path components: ODs are generalized into regions to satisfy $k$k-anonymity, and paths are sanitized within each OD group using road-network-aware differential privacy under predefined privacy constraints. We also develop a visual interface to support exploration and comprehension of privacy-preserving solutions, through which we incorporate human knowledge into the privacy scheme. Experiments on real-world urban datasets demonstrate the effectiveness of our approach.

Concept management for large-scale text data is critical in domains such as healthcare informatics, digital libraries, and news classification. However, the variability in concept structures and the diversity of application requirements pose challenges for existing automated methods, which often lack the flexibility to accommodate customized needs. Meanwhile, manual classification remains resource-intensive and inefficient. To address this issue, we propose CateSift, an interactive approach that integrates public knowledge to streamline the classification process and incorporates expert knowledge to formulate classification models. The main contributions of this work are as follows: (1) a visualization interface, called CateSift , that facilitates users in constructing and refining classification models for large-scale data, and (2) A prompt-based model that can integrate expert knowledge to iteratively refine hierarchical classification structures. Specifically, CateSift provides users with a hierarchical concept tree that highlights concepts with uncertain classifications and invites users to optimize the classification models by injecting knowledge. To address the issue of large-scale data, CateSift allows users to steer the classification model by adjusting the classification tree or annotating classifications. Case studies indicate that the proposed approach effectively and efficiently supports classification for large-scale data. • This study presents an interactive classification framework, CateSift , which employs multi-level prompt templates and integrates prompt-based large language models with expert knowledge to perform hierarchical classification on large-scale datasets. This method effectively overcomes the limitations of existing automated approaches regarding accuracy and hierarchical structure flexibility, as well as the inefficiencies associated with manual classification. • An interactive prototype system is introduced to support users in interpreting and guiding model classification outcomes, enabling efficient detection of potential errors and unstable hierarchical components. The system facilitates iterative model refinement through user-provided corrections and annotations, accommodating diverse domain-specific needs and further decreasing user effort.

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

This paper presents GraphFederator, a novel approach to construct federated representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for federated modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a Federated Graph Representation Model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization tools for federated visualization, exploration, and analysis of multi-party graphs. Experimental results on two datasets demonstrate the effectiveness of our approach.

Differential privacy is an essential approach for privacy preservation in data queries. However, users face a significant challenge in selecting an appropriate privacy scheme, as they struggle to balance the utility of query results with the preservation of diverse individual privacy. Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes. When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values, data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes. In this paper, we propose a visual analysis approach for formulating privacy schemes of differential privacy. Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases. We also developed a prototype system, called DPKnob, which integrates multiple coordinated views. DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high-risk attacks, but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy. This iterative process allows users to compare different schemes, refine their expectations of privacy and utility, and ultimately establish a well-balanced privacy scheme. The effectiveness of this study is verified by a user study and two case studies with real-world datasets.

Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions. Despite enhancing robustness, adversarial training often results in a trade-off with standard accuracy on normal data, a phenomenon that remains a contentious issue. In addition, the opaque nature of deep neural network models renders it more difficult to inspect and diagnose how adversarial training processes evolve. This paper introduces ATVis, a visual analytics framework for examining and diagnosing adversarial training processes. Through multi-level visualization design, ATVis enables the examination of model robustness from various granularity, facilitating a detailed understanding of the dynamics in the training epochs. The framework reveals the complex relationship between adversarial robustness and standard accuracy, which further offers insights into the mechanisms that drive the trade-offs observed in adversarial training. The effectiveness of the framework is demonstrated through case studies.

A non-fungible token (NFT) is a data unit stored on the blockchain. Nowadays, more and more investors and collectors (NFT traders), who participate in transactions of NFTs, have an urgent need to assess the performance of NFTs. However, there are two challenges for NFT traders when analyzing the performance of NFT. First, the current rarity models have flaws and are sometimes not convincing. In addition, NFT performance is dependent on multiple factors, such as images (high-dimensional data), history transactions (network), and market evolution (time series). It is difficult to take comprehensive consideration and analyze NFT performance efficiently. To address these challenges, we propose NFTVis, a visual analysis system that facilitates assessing individual NFT performance. A new NFT rarity model is proposed to quantify NFTs with images. Four well-coordinated views are designed to represent the various factors affecting the performance of the NFT. Finally, we evaluate the usefulness and effectiveness of our system using two case studies and user studies.

Abstract Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On one hand, visualization can facilitate humans in data understanding through intuitive visual representation and interactive exploration. On the other hand, AI is able to learn from data and implement bulky tasks for humans. In complex data analysis scenarios, like epidemic traceability and city planning, humans need to understand large-scale data and make decisions, which requires complementing the strengths of both visualization and AI. Existing studies have introduced AI-assisted visualization as AI4VIS and visualization-assisted AI as VIS4AI. However, how can AI and visualization complement each other and be integrated into data analysis processes are still missing. In this paper, we define three integration levels of visualization and AI. The highest integration level is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces. We also summarize future directions of VIS+AI to inspire related studies.

Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis.

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

Collecting and analyzing anonymous personal information is required as a part of data analysis processes, such as medical diagnosis and restaurant recommendation. Such data should ostensibly be stored so that specific individual information cannot be disclosed. Unfortunately, inference attacks-integrating background knowledge and intelligent models-hinder classic sanitization techniques like syntactic anonymity and differential privacy from exhaustively protecting sensitive information. As a solution, we introduce a three-stage approach empowered within a visual interface, which depicts underlying inference behaviors via a Bayesian Network and supports a customized defense against inference attacks from unknown adversaries. In particular, our approach visually explains the process details of the underlying privacy preserving models, allowing users to verify if the results sufficiently satisfy the requirements of privacy preservation. We demonstrate the effectiveness of our approach through two case studies and expert reviews.

三元空间大数据一般定义为由其定义领域 (包括数据、 对象、 任务、 应用场景、 主体等) 所有元素组成的集合. 可视分析是一种新兴的人在回路大数据分析范式, 可利用人类感知提高人类认知效率. 本文探讨三元空间大数据跨域可视化分析, 强调三元空间大数据跨域性带来的新挑战——数据、 主题和任务域, 并提出一个新的可视分析模型和一套方法来应对这些挑战.

Research on intelligent vehicles has been popular in the past decade. To fill the gap between automatic approaches and man-machine control systems, it is indispensable to integrate visual human-computer interactions (VHCIs) into intelligent vehicles systems. In this article, we review existing studies on VHCI in intelligent vehicles from three aspects: 1) visual intelligence; 2) decision making; and 3) macro deployment. We discuss how VHCI evolves in intelligent vehicles and how it enhances the capability of intelligent vehicles. We present several simulated scenarios and cases for future intelligent transportation system.

The increased accessibility of urban sensor data and the popularity of social network applications is enabling the discovery of crowd mobility and personal communication patterns. However, studying the egocentric relationships of an individual can be very challenging because available data may refer to direct contacts, such as phone calls between individuals, or indirect contacts, such as paired location presence. In this article, we develop methods to integrate three facets extracted from heterogeneous urban data (timelines, calls, and locations) through a progressive visual reasoning and inspection scheme. Our approach uses a detect-and-filter scheme such that, prior to visual refinement and analysis, a coarse detection is performed to extract the target individual and construct the timeline of the target. It then detects spatio-temporal co-occurrences or call-based contacts to develop the egocentric network of the individual. The filtering stage is enhanced with a line-based visual reasoning interface that facilitates a flexible and comprehensive investigation of egocentric relationships and connections in terms of time, space, and social networks. The integrated system, RelationLines, is demonstrated using a dataset that contains taxi GPS data, cell-base mobility data, mobile calling data, microblog data, and point-of-interest (POI) data from a city with millions of citizens. We examine the effectiveness and efficiency of our system with three case studies and user review.

Visual analysis is widely applied to study human mobility due to the ability of integrating contextual information from multiple data sources. Analyzing trajectory data through visualization improves the efficiency and accuracy of the analysis, yet it may induce exposure of the location privacy. To balance the location privacy and analysis effectiveness, this work focuses on the behaviors of different geo-based contexts in the process of trajectory interpretation. Three types of geo-based contexts are identified after surveying 94 related literatures. We further conduct experiments to investigate their capability by evaluating how they benefit the analysis, and whether they lead to location privacy exposure. Finally, we report and discuss interesting findings, and provide guidelines to the design of privacy-preserving analysis approaches for human periodic trajectories.

Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques-along with evaluating how applying the strategies will affect the utility of the anonymized results-remains a significant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.

Sharing data for public usage requires sanitization to prevent sensitive information from leaking. Previous studies have presented methods for creating privacy preserving visualizations. However, few of them provide sufficient feedback to users on how much utility is reduced (or preserved) during such a process. To address this, we design a visual interface along with a data manipulation pipeline that allows users to gauge utility loss while interactively and iteratively handling privacy issues in their data. Widely known and discussed types of privacy models, i.e., syntactic anonymity and differential privacy, are integrated and compared under different use case scenarios. Case study results on a variety of examples demonstrate the effectiveness of our approach.