A Case Study of Anonymization of Medical Surveys

Gentili, Michele; Hajian, Sara; Castillo, Carlos

doi:10.1145/3079452.3079490

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Furthermore, a proportion of identification risk comes from the presence of other auxiliary information, for instance, in neuroimaging, the scanner used to acquire the image. This is known as linkage attack, and is increasingly difficult to protect against across fields using classic anonymisation techniques (Sweeney, 2002;Gentili et al, 2017;Bindschaedler et al, 2018).…”

Section: Data Sharing and Data Privacymentioning

confidence: 99%

Challenges for machine learning in clinical translation of big data imaging studies

Dinsdale¹,

Bluemke²,

Sundaresan³

et al. 2021

Preprint

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The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the success of deep learning when applied to many neuroimaging tasks, there remain barriers to the clinical translation of large-scale datasets and processing tools. Here, we explore the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation and logistical challenges, and discuss the challenges we believe are still to be overcome to enable the full success of big data deep learning approaches to be experienced outside of the research field.

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Section: Data Sharing and Data Privacymentioning

confidence: 99%

Challenges for machine learning in clinical translation of big data imaging studies

Dinsdale¹,

Bluemke²,

Sundaresan³

et al. 2021

Preprint

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“…Although sharing health data may be useful for a variety of stakeholders, such as researchers, governments, and health-care providers, there are intricate regulations and perceptions, which shape the ways the public can share health data. The European Data Protection Directive has leveraged data privacy of individuals, which allows them to enforce access restrictions on the data they share, this type of regulation creates a strong demand for balance between security and utility of data [13]. Sharing health data is especially important between patients and healthcare providers, where such data can dramatically increase the number and quality of insights that healthcare providers are able to deliver [18].…”

Section: Health Datamentioning

confidence: 99%

Large displays and tablets

Gorkovenko

Lischke

Woźniak

2018

Proceedings of the 10th Nordic Conference on Human-Computer Interaction

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Data is pivotal to open government initiatives, where citizens are often expected to be informed and actively participate. Yet, it can be difficult for people to understand the meaning of data. Presenting data to the public in an appropriate way may also increase citizen's willingness to participate in data collection. Here we present a study which explores how large screens can support socially relevant data exploration. In a between subject laboratory experiment, we analysed how pairs of participants explored data visualisations on a highresolution display (LHRD) and a tablet. Our results indicate that LHRDs are less cognitively demanding, while tablets offer more shared control of the interface. Data exploration had limited effect on increasing comfort with sharing personal data but helped increase perceptions of trustworthiness within the data collection process. We observed that appropriately visualised data on either platform has significant potential to increase the public's understanding of large data sets.

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“…The GDPR rules are paramount for companies to apply even though they have shown to be challenging for sharing and anonymising data [149,150]. As stated, different types of methods and studies in anonymising data within the health sector have been conducted [151][152][153]. Nonetheless, the study found no prior studies addressing sharing and anonymising manufacturing data regarding machine learning.…”

Section: Extended Abstract Introduction and Company Perspectivementioning

confidence: 99%

AI and IoT for Production Data Analytics in SMEs

Hansen¹

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A Case Study of Anonymization of Medical Surveys

Cited by 7 publications

References 7 publications

Challenges for machine learning in clinical translation of big data imaging studies

Challenges for machine learning in clinical translation of big data imaging studies

Large displays and tablets

AI and IoT for Production Data Analytics in SMEs

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