The Governance of Privacy

2021 ◽  
Author(s):  
Hans Bruijn
Keyword(s):  
Privacy Protection ◽  
Large Scale ◽  
Personal Data ◽  
Data Driven ◽  
Privacy Breach ◽  
Share Data ◽  
De Bruijn ◽  
The Law ◽  
Privacy Breaches

We can hardly underestimate the importance of privacy in our data-driven world. Privacy breaches are not just about disclosing information. Personal data is used to profile and manipulate us – sometimes on such a large scale that it affects society as a whole. What can governments do to protect our privacy? In The Governance of Privacy Hans de Bruijn first analyses the complexity of the governance challenge, using the metaphor of a journey. At the start, users have strong incentives to share data. Harvested data continue the journey that might lead to a privacy breach, but not necessarily – it can also lead to highly valued services. That is why both preparedness at the start of the journey and resilience during the journey are crucial to privacy protection. The book then explores three strategies to deal with governments, the market, and society. Governments can use the power of the law; they can exploit the power of the market by stimulating companies to compete on privacy; and they can empower society, strengthening its resilience in a data-driven world.

scholarly journals Evaluating privacy of individuals in medical data

2021 ◽  
Vol 27 (2) ◽  
pp. 146045822098339
Author(s):  
Shannon KS Kroes ◽  
Mart P Janssen ◽  
Rolf HH Groenwold ◽  
Matthijs van Leeuwen
Keyword(s):  
European Union ◽  
Personal Data ◽  
Medical Data ◽  
Sensitive Information ◽  
The European Union ◽  
Systematic Method ◽  
Privacy Breach ◽  
Privacy Analysis ◽  
Privacy Breaches ◽  
Visualization Techniques

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.

Importance of Personal Data Protection Law for Commercial Air Transport

2017 ◽  
Vol 2017 (1) ◽  
pp. 35-44
Author(s):  
Dawid Zadura
Keyword(s):  
Data Protection ◽  
Public Information ◽  
Personal Data ◽  
Air Transport ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Personal Data Protection ◽  
It Systems ◽  
Data Protection Law ◽  
The Law

Abstract In the review below the author presents a general overview of the selected contemporary legal issues related to the present growth of the aviation industry and the development of aviation technologies. The review is focused on the questions at the intersection of aviation law and personal data protection law. Massive processing of passenger data (Passenger Name Record, PNR) in IT systems is a daily activity for the contemporary aviation industry. Simultaneously, since the mid- 1990s we can observe the rapid growth of personal data protection law as a very new branch of the law. The importance of this new branch of the law for the aviation industry is however still questionable and unclear. This article includes the summary of the author’s own research conducted between 2011 and 2017, in particular his audits in LOT Polish Airlines (June 2011-April 2013) and Lublin Airport (July - September 2013) and the author’s analyses of public information shared by International Civil Aviation Organization (ICAO), International Air Transport Association (IATA), Association of European Airlines (AEA), Civil Aviation Authority (ULC) and (GIODO). The purpose of the author’s research was to determine the applicability of the implementation of technical and organizational measures established by personal data protection law in aviation industry entities.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

Data-Driven Lightweight Interest Point Selection for Large-Scale Visual Search

2018 ◽  
Vol 20 (10) ◽  
pp. 2774-2787 ◽  
Author(s):  
Feng Gao ◽  
Xinfeng Zhang ◽  
Yicheng Huang ◽  
Yong Luo ◽  
Xiaoming Li ◽  
...  
Keyword(s):  
Visual Search ◽  
Large Scale ◽  
Data Driven ◽  
Interest Point ◽  
Point Selection ◽  
Selection For

scholarly journals Limits of data anonymity: lack of public awareness risks trust in health system activities

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Felix Gille ◽  
Caroline Brall
Keyword(s):  
Large Scale ◽  
Public Awareness ◽  
Personal Data ◽  
Public Trust ◽  
Online News ◽  
Healthcare Management ◽  
Contact Tracing ◽  
Common Denominator ◽  
The Public ◽  
Privacy And Anonymity

AbstractPublic trust is paramount for the well functioning of data driven healthcare activities such as digital health interventions, contact tracing or the build-up of electronic health records. As the use of personal data is the common denominator for these healthcare activities, healthcare actors have an interest to ensure privacy and anonymity of the personal data they depend on. Maintaining privacy and anonymity of personal data contribute to the trustworthiness of these healthcare activities and are associated with the public willingness to trust these activities with their personal data. An analysis of online news readership comments about the failed care.data programme in England revealed that parts of the public have a false understanding of anonymity in the context of privacy protection of personal data as used for healthcare management and medical research. Some of those commenting demanded complete anonymity of their data to be willing to trust the process of data collection and analysis. As this demand is impossible to fulfil and trust is built on a false understanding of anonymity, the inability to meet this demand risks undermining public trust. Since public concerns about anonymity and privacy of personal data appear to be increasing, a large-scale information campaign about the limits and possibilities of anonymity with respect to the various uses of personal health data is urgently needed to help the public to make better informed choices about providing personal data.

Large-scale experiments on data-driven design of commercial spoken dialog systems

2011 ◽  
Author(s):  
D. Suendermann ◽  
J. Liscombe ◽  
J. Bloom ◽  
G. Li ◽  
Roberto Pieraccini
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Spoken Dialog Systems ◽  
Dialog Systems
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