DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Zhou, Jiehui; Xumeng, Wang,; Wong, Jason K.; Wang, Huanliang; Wang, Zhongwei; Yang, Xiaoyu; Yan, Xiaoran; Feng, Haozhe; Qu, Huamin; Ying, Haochao; Chen, Wei

doi:10.1109/tvcg.2022.3209391

Cited by 11 publications

(3 citation statements)

References 68 publications

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“…For example, event features (e.g., business procedures) and other behavioral features (e.g., machine operations and agent behaviors) provide contexts for causal analysis to satisfaction. Screen-space and data-space sanitization techniques [68] could be useful in protecting clients' privacy. We have also collected multi-view videos about the clients which can provide additional viewpoints to infer their actions.…”

Section: Discussionmentioning

confidence: 99%

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Kwai¹,

Wang²,

Wang³

et al. 2023

Preprint

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Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce Anchorage, a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. Anchorage supports a comprehensive evaluation of customer satisfaction from the service and operation levels and efficient analysis of customer behavioral dynamics via multifaceted visualization views. We extensively evaluate Anchorage through a case study and a carefully-designed user study. The results demonstrate its effectiveness and usability in assessing customer satisfaction using customer service videos. We found that introducing event contexts in assessing customer satisfaction can enhance its performance without compromising annotation precision. Our approach can be adapted in situations where unlabelled and unstructured videos are collected along with sequential records.

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Section: Discussionmentioning

confidence: 99%

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Kwai¹,

Wang²,

Wang³

et al. 2023

Preprint

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show abstract

“…In recent years, there has been a significant amount of work in the visualization field related to mixed-initiative visual analytics systems. [59][60][61][62][63][64][65][66][67][68][69] These systems leverage both human and machine intelligence to explore and analyze complex data utilizing innovative visual designs. [70][71][72][73][74][75][76] For instance, Wall et al 77 proposed the Podium system that enabled users to rank multi-attribute data based on their holistic understanding and explore their subjective preferences.…”

Section: Mixed-initiative Visual Analytics Systemsmentioning

confidence: 99%

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Weng,

Liu,

Zhu

et al. 2024

Information Visualization

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Large language models (LLMs) have gained popularity in various fields for their exceptional capability of generating human-like text. Their potential misuse has raised social concerns about plagiarism in academic contexts. However, effective artificial scientific text detection is a non-trivial task due to several challenges, including (1) the lack of a clear understanding of the differences between machine-generated and human-written scientific text, (2) the poor generalization performance of existing methods caused by out-of-distribution issues, and (3) the limited support for human-machine collaboration with sufficient interpretability during the detection process. In this paper, we first identify the critical distinctions between machine-generated and human-written scientific text through a quantitative experiment. Then, we propose a mixed-initiative workflow that combines human experts’ prior knowledge with machine intelligence, along with a visual analytics system to facilitate efficient and trustworthy scientific text detection. Finally, we demonstrate the effectiveness of our approach through two case studies and a controlled user study. We also provide design implications for interactive artificial text detection tools in high-stakes decision-making scenarios.

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“…The work by Zhou et al [72] uses this definition of visual utility when designing an interactive system to modify private synthetic data generators. Parts of their interface evaluate how well the visual patterns are preserved.…”

Section: Visual Utilitymentioning

confidence: 99%

Investigating the Visual Utility of Differentially Private Scatterplots

Panavas

Crnovrsanin

Adams

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection. A free copy of this paper along with all supplemental materials is available at https://osf.io/wej4s/.

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DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Cited by 11 publications

References 68 publications

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Anchorage: Visual Analysis of Satisfaction in Customer Service Videos via Anchor Events

Towards an understanding and explanation for mixed-initiative artificial scientific text detection

Investigating the Visual Utility of Differentially Private Scatterplots

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