scholarly journals Regulating Telematics Insurance

2020 ◽  
pp. 293-325
Author(s):  
Freyja van den Boom
Keyword(s):  
Insurance Industry ◽  
Personal Data ◽  
Driving Behavior ◽  
Risk Assessments ◽  
Data Driven ◽  
Information Requirements ◽  
Privacy Concerns ◽  
Vehicle Data ◽  
Broad Interpretation ◽  
The Many

Abstract‘Telematics’ insurance is an example of data driven innovation in the insurance industry where data obtained from the vehicle (such as speed, time and location) is used to provide consumers with premiums based on their actual driving behavior. Despite the many benefits including more accurate risk assessments and premium setting, there are serious privacy concerns about the increased use of vehicle data for insurance purposes. The information requirements of the GDPR and the IDD could address some of these concerns in the context of telematics insurance. This research chapter concludes the analysis of the scope of these requirements by proposing the need for a broad interpretation for information to be made available in order to effectively help consumers make better, well informed decisions about insurance products and use of their personal data for insurance purposes.

scholarly journals Digital Economy, Big Data and Competition Law

2019 ◽  
Vol 3 (1) ◽  
pp. 53-89
Author(s):  
Roberto Augusto Castellanos Pfeiffer
Keyword(s):  
Big Data ◽  
Data Protection ◽  
Personal Data ◽  
Competition Law ◽  
Digital Economy ◽  
Data Driven ◽  
Dominant Position ◽  
Privacy Concerns ◽  
Sense Data ◽  
Abuse Of Dominant Position

Big data has a very important role in the digital economy, because firms have accurate tools to collect, store, analyse, treat, monetise and disseminate voluminous amounts of data. Companies have been improving their revenues with information about the behaviour, preferences, needs, expectations, desires and evaluations of their consumers. In this sense, data could be considered as a productive input. The article focuses on the current discussion regarding the possible use of competition law and policy to address privacy concerns related to big data companies. The most traditional and powerful tool to deal with privacy concerns is personal data protection law. Notwithstanding, the article examines whether competition law should play an important role in data-driven markets where privacy is a key factor. The article suggests a new approach to the following antitrust concepts in cases related to big data platforms: assessment of market power, merger notification thresholds, measurement of merger effects on consumer privacy, and investigation of abuse of dominant position. In this context, the article analyses decisions of competition agencies which reviewed mergers in big data-driven markets, such as Google/DoubleClick, Facebook/ WhatsApp and Microsoft/LinkedIn. It also reviews investigations of alleged abuse of dominant position associated with big data, in particular the proceeding opened by the Bundeskartellamt against Facebook, in which the German antitrust authority prohibited the data processing policy imposed by Facebook on its users. The article concludes that it is important to harmonise the enforcement of competition, consumer and data protection polices in order to choose the proper way to protect the users of dominant platforms, maximising the benefits of the data-driven economy.

scholarly journals GDPR: Is your consent valid?

2020 ◽  
Vol 37 (1) ◽  
pp. 19-24
Author(s):  
Stephen Breen ◽  
Karim Ouazzane ◽  
Preeti Patel
Keyword(s):  
Information Systems ◽  
Data Protection ◽  
Personal Data ◽  
Data Driven ◽  
Privacy Concerns ◽  
General Data Protection Regulation ◽  
Philosophical Background ◽  
General Data ◽  
Compliance With The Law ◽  
Point Of Departure

The General Data Protection Regulation (GDPR) 2018 imposes much greater demands on companies to address the rights of individuals who provide data, that is, Data Subjects. The new law requires a much more transparent approach to gaining consent to process personal data. However, few obvious changes to how consent is gained from Data Subjects to comply with this. Many companies are running the risk of non-compliance with the law if they fail to address how data are obtained and the lack of true consent which Data Subjects currently give to their data being processed. Consent is a complex philosophical principle which relies on the person giving the consent being in full possession of the facts, this article explores the philosophical background of consent and examines the circumstances which were the point of departure for the debate on consent and attempts to develop an understanding of it in the context of the growing influence of information systems and the data-driven economy. The GDPR has gone further than any other regulation or law to date in developing an understanding of consent to address personal data and privacy concerns.

scholarly journals Conceptualising value creation in data-driven services: The case of vehicle data

2021 ◽  
Vol 59 ◽  
pp. 102335
Author(s):  
Christian Kaiser ◽  
Alexander Stocker ◽  
Gianluigi Viscusi ◽  
Michael Fellmann ◽  
Alexander Richter
Keyword(s):  
Value Creation ◽  
Data Driven ◽  
Vehicle Data

scholarly journals Development of Personal Information Privacy Concerns Evaluation

2018 ◽  
pp. 4862-4871
Author(s):  
Anna Rohunen ◽  
Jouni Markkula
Keyword(s):  
Service Providers ◽  
Personal Information ◽  
Personal Data ◽  
Future Research ◽  
Privacy Concerns ◽  
The Public ◽  
Data Intensive ◽  
Information Privacy Concerns ◽  
Evaluation Instruments ◽  
Information And Communication

Personal data is increasingly collected with the support of rapidly advancing information and communication technology, which raises privacy concerns among data subjects. In order to address these concerns and offer the full benefits of personal data intensive services to the public, service providers need to understand how to evaluate privacy concerns in evolving service contexts. By analyzing the earlier used privacy concerns evaluation instruments, we can learn how to adapt them to new contexts. In this article, the historical development of the most widely used privacy concerns evaluation instruments is presented and analyzed regarding privacy concerns' dimensions. Privacy concerns' core dimensions, and the types of context dependent dimensions, to be incorporated into evaluation instruments are identified. Following this, recommendations on how to utilize the existing evaluation instruments are given, as well as suggestions for future research dealing with validation and standardization of the instruments.

Data Lives

2021 ◽  
Author(s):  
Rob Kitchin
Keyword(s):  
Social Relations ◽  
Human Values ◽  
Data Driven ◽  
Daily Lives ◽  
Digital Devices ◽  
Digital Ethics ◽  
Political Forces ◽  
Scientific Principles ◽  
Generate Process ◽  
The Many

How can we begin to grasp the scope and scale of our new data-rich world, and can we truly comprehend what is at stake? This book explores the intricacies of data creation and charts how data-driven technologies have become essential to how society, government and the economy work. Creatively blending scholarly analysis, biography and fiction, the book demonstrates how data are shaped by social and political forces, and the extent to which they influence our daily lives. The book begins with an overview of the sociality of data. Data-driven endeavours are as much a result of human values, desires, and social relations as they are scientific principles and technologies. The data revolution has been transforming work and the economy, the nature of consumption, the management and governance of society, how we communicate and interact with media and each other, and forms of play and leisure. Indeed, our lives are saturated with digital devices and services that generate, process, and share vast quantities of data. The book reveals the many, complex, contested ways in which data are produced and circulated, as well as the consequences of living in a data-driven world. The book concludes with an exploration as to what kind of data future we want to create and strategies for realizing our visions. It highlights the need to enact 'a digital ethics of care', and to claim and assert 'data sovereignty'. Ultimately, the book reveals our data world to be one of potential danger, but also of hope.

Dataveillance in the Workplace

2019 ◽  
pp. 391-410
Author(s):  
Regina Connolly ◽  
Cliona McParland
Keyword(s):  
Psychological Contract ◽  
Employee Performance ◽  
Counterproductive Work Behaviors ◽  
Ethical Framework ◽  
Employee Privacy ◽  
Privacy Concerns ◽  
Employee Trust ◽  
Emerging Issues ◽  
The Many ◽  
Monitoring Technologies

The many obvious benefits that accompany digital technology have been matched by some less welcome and more contentious impacts. One of these is the steady erosion of employee privacy. Whilst employee performance has frequently been the object of scrutiny, the increasing number of organizations that monitor employees through advanced digital technologies has added a dystopian edge to existing employee privacy concerns, particularly as many employees are unable to exercise choice in relation to use of these technologies. If unaddressed, their concerns have potential to impact the psychological contract between employee and employer, resulting in loss of employee trust, negative attitudes, and counterproductive work behaviors. This chapter outlines some of the emerging issues relating to use of employee monitoring technologies. It summarizes both management rationale for monitoring as well as employee privacy concerns and proposes an ethical framework that is useful for balancing these differing perspectives.

A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 378-389
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

Will it be Disclosure or Fabrication of Personal Information?

2010 ◽  
pp. 231-254 ◽  
Author(s):  
Xun Li ◽  
Radhika Santhanam
Keyword(s):  
Personal Information ◽  
Personal Data ◽  
Job Applicants ◽  
Accurate Information ◽  
Hiring Decisions ◽  
Job Applicant ◽  
Privacy Concerns ◽  
Willingness To Disclose ◽  
Information Sensitivity ◽  
Theoretical Foundations

Individuals are increasingly reluctant to disclose personal data and sometimes even intentionally fabricate information to avoid the risk of having it compromised. In this context, organizations face an acute dilemma: they must obtain accurate job applicant information in order to make good hiring decisions, but potential employees may be reluctant to provide accurate information because they fear it could be used for other purposes. Building on theoretical foundations from social cognition and persuasion theory, we propose that, depending on levels of privacy concerns, organizations could use appropriate strategies to persuade job applicants to provide accurate information. We conducted a laboratory experiment to examine the effects of two different persuasion strategies on prospective employees’ willingness to disclose information, measured as their intentions to disclose or falsify information. Our results show support for our suggestion As part of this study, we propose the term information sensitivity to identify the types of personal information that potential employees are most reluctant to disclose.

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