PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data

Bhattacharjee, Kaustav; Islam, Akm; Vaidya, Jaideep; Dasgupta, Aritra

doi:10.1109/vizsec56996.2022.9941431

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Collaborative efforts are critical because open data projects frequently involve several stakeholders, which promotes knowledge exchange and resource sharing. User-friendly data portals, as advocated by the Open Data Charter, are required for efficient data sharing [42]. It is critical to ensure that data is easily accessible, downloadable, and machine-readable.…”

Section: Literature Reviewmentioning

confidence: 99%

The Improvement of the Use of Open Data in Public Institutions

Hyseni,

Bexheti

2024

Preprint

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The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

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Section: Literature Reviewmentioning

confidence: 99%

The Improvement of the Use of Open Data in Public Institutions

Hyseni,

Bexheti

2024

Preprint

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“…Computing embeddings is often at the beginning of a VA pipeline to unify input data into the same vector space, particularly for anomaly detection and risk inspection. In PRIVEE [BIVD22], embeddings are used to find and represent joinable datasets. Additional operations, such as weighting factors, can be incorporated into embedding vectors to convey additional customized, attribute‐based information.…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low‐dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer‐reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.

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“…Sawyer et al observed that the performance of human observers deteriorates over time in a low-signal vigilance scenario, which is a likely scenario for data defenders [32], who are faced with the arduous task of finding needles, i.e., privacy vulnerabilities, in the unsuspecting haystack of linkable open data. As our third contribution, we discuss how vulnerabilities can be detected and triaged using visual analytic interventions [2] that can serve as cognitive aid for data defenders for continuous monitoring of privacy risks. We focus on the vulnerabilities discovered and their possible remediation through visual analytic solutions (Section IV).…”

Section: Introductionmentioning

confidence: 99%

Power to the Data Defenders: Human-Centered Disclosure Risk Calibration of Open Data

Bhattacharjee¹,

Dasgupta²

2023

Proceedings 2023 Symposium on Usable Security

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The open data ecosystem is susceptible to vulnerabilities due to disclosure risks. Though the datasets are anonymized during release, the prevalence of the release-andforget model makes the data defenders blind to privacy issues arising after the dataset release. One such issue can be the disclosure risks in the presence of newly released datasets which may compromise the privacy of the data subjects of the anonymous open datasets. In this paper, we first examine some of these pitfalls through the examples we observed during a red teaming exercise and then envision other possible vulnerabilities in this context. We also discuss proactive risk monitoring, including developing a collection of highly susceptible open datasets and a visual analytic workflow that empowers data defenders towards undertaking dynamic risk calibration strategies.

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PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data

Cited by 6 publications

References 32 publications

The Improvement of the Use of Open Data in Public Institutions

The Improvement of the Use of Open Data in Public Institutions

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Power to the Data Defenders: Human-Centered Disclosure Risk Calibration of Open Data

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