Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Bhattacharjee, Kaustav; Chen, Min; Dasgupta, Aritra

doi:10.1111/cgf.14032

Cited by 25 publications

(16 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such considerations should limit the number of simultaneous visuals and interactivity that might enable triangulation, reidentification, and personal data exposure. In line with Figure 1, this point highlights the importance of recognizing how various use cases also differ by visual consumer (Bhattacharjee et al 2020).…”

Section: Continuing and Emerging Concernssupporting

confidence: 73%

Visualization in Operations Management Research

Basole

Bendoly

Chandrasekaran

et al. 2022

INFORMS Journal on Data Science

View full text Add to dashboard Cite

The unprecedented availability of data, along with the growing variety of software packages to visualize it, presents both opportunities and challenges for operations management (OM) research. OM researchers typically use data to describe conditions, predict phenomena, or make prescriptions depending on whether they are building, testing, or translating theories to practice. Visualization, when used appropriately, can complement, aid, and augment the researcher’s understanding in the different stages of research (theory building, testing, or translating and conveying results). On the other hand, if used incorrectly or without sufficient consideration, visualization can yield misleading and erroneous claims. This article formally examines the benefits of visualization as a complementary method enhancing each stage of a broader OM research strategy by examining frameworks and cases from extant research in different OM contexts. Our discussion offers guidance with regard to researchers’ use of visual data renderings, particularly toward avoiding misrepresentation, which can arise with the incorrect use of visualization. We close with a consideration of emerging trends and their implications for researchers and practitioners as well as recommendations for both authors and reviewers, regardless of domain, in evaluating the effectiveness of visuals at each stage of research.

show abstract

Section: Continuing and Emerging Concernssupporting

confidence: 73%

Visualization in Operations Management Research

Basole

Bendoly

Chandrasekaran

et al. 2022

INFORMS Journal on Data Science

View full text Add to dashboard Cite

show abstract

“…Human Face is a category of objects with high privacy sensitivity. Recently, privacy concerns for human faces in images have received much attention [17,20,25,[38][39][40][41][42][43]. Several studies have focused on the balance between privacy protection and data utility [16,18,19,[44][45][46].…”

Section: Privacy Concern For Facial Informationmentioning

confidence: 99%

Reliable metrics‐based linear regression model for multilevel privacy measurement of face instances

Huang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Social networking sites have made photo sharing convenient and, consequently, users of those sites frequently share photos. The proliferation of these social images throughout the Internet has inadvertently exposed an increasing number of personally identifiable information, particularly from the visual information of the faces in the images. Most existing methods to de-identify faces results in an excessive loss of visual information. To solve this problem, this paper proposes a reliable metrics-based linear regression model for multilevel privacy measurement of face instances using the size information of face instances. The proposed privacy measurement model provides a novel instance-level-based solution to measure privacy levels. The paper also establishes a scientific relationship between the size information of face instances and their privacy levels, quantifying the degree to which face instances need to be de-identified. Finally, the paper proposes a novel k-Same-DT de-identification method to provide reliable metrics for a linear regression model. It is a real-time k-same-related de-identification algorithm that combines the PCA dimensionality reduction strategy and the Delaunay triangle-based face alignment algorithm.The proposed k-Same-DT method creates a de-identified face with a lower identifiable rate and higher structural similarity, and it can provide reliable de-identification metrics for privacy measurement. Experiments using the classical face dataset demonstrates the effectiveness of the proposed de-identification method. Extensive experiments and surveys on real-world social images were also conducted to verify the proposed measurement model.

show abstract

“…It often happens with crowdsensing and crowdsourcing platforms [19]. This, in turn, is one of the many obstacles to the early integration of smart city services [20,21]. In such cases, city dwellers are inactive information providers and show no interest in crowdsourcing campaigns [22].…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

Liu

Panfilova

Mikhaylov

et al. 2021

Wireless Pers Commun

View full text Add to dashboard Cite

This research reviews challenges in building sustainable relationships between the parties involved in the crowdfunding and crowdsourcing projects, which are running in extreme situations, such as the COVID-19 pandemic. This study aims to solve problems that generate the crowdsourcing concerns and to find better alternatives to increase trust for crowdfunding among donors, as this impacts their strategic sustainability in the conditions of turbulence and COVID-induced financial crisis. It was found that factors influence donor decisions in different ways, yet the common tendency for donor activity is non-monotonicity. Future development in the field of sustainable relationships should focus on creating a donor classification system.

show abstract

Privacy‐Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities

Cited by 25 publications

References 94 publications

Visualization in Operations Management Research

Visualization in Operations Management Research

Reliable metrics‐based linear regression model for multilevel privacy measurement of face instances

COVID-19 crisis impact on the stability between parties in crowdfunding and crowdsourcing

Contact Info

Product

Resources

About