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Components and Architecture for the Implementation of Technology-Driven Employee Data Protection
(2021)
The ongoing digitisation in everyday working life means that ever larger amounts of personal data of employees are processed by their employers. This development is particularly problematic with regard to employee data protection and the right to informational self-determination. We strive for the use of company Privacy Dashboards as a means to compensate for missing transparency and control. For conceptual design we use among other things the method of mental models. We present the methodology and first results of our research. We highlight the opportunities that such an approach offers for the user-centred development of Privacy Dashboards.
Applied privacy research has so far focused mainly on consumer relations in private life. Privacy in the context of employment relationships is less well studied, although it is subject to the same legal privacy framework in Europe. The European General Data Protection Regulation (GDPR) has strengthened employees’ right to privacy by obliging that employers provide transparency and intervention mechanisms. For such mechanisms to be effective, employees must have a sound understanding of their functions and value. We explored possible boundaries by conducting a semistructured interview study with 27 office workers in Germany and elicited mental models of the right to informational self-determination, which is the European proxy for the right to privacy. We provide insights into (1) perceptions of different categories of data, (2) familiarity with the legal framework regarding expectations for privacy controls, and (3) awareness of data processing, data flow, safeguards, and threat models. We found that legal terms often used in privacy policies used to describe categories of data are misleading. We further identified three groups of mental models that differ in their privacy control requirements and willingness to accept restrictions on their privacy rights. We also found ignorance about actual data flow, processing, and safeguard implementation. Participants’ mindsets were shaped by their faith in organizational and technical measures to protect privacy. Employers and developers may benefit from our contributions by understanding the types of privacy controls desired by office workers and the challenges to be considered when conceptualizing and designing usable privacy protections in the workplace.
Continuous authentication has emerged as a promising approach to increase user account security for online services. Unlike traditional authentication methods, continuous authentication provides ongoing security throughout the session, protecting against session takeover attacks due to illegitimate access. The effectiveness of continuous authentication systems relies on the continuous processing of users' sensitive biometric data. To balance security and privacy trade-offs, it's crucial to understand when users are willing to disclose biometric data for enhanced account security, addressing inevitable privacy concerns and user acceptance. To address this knowledge gap, we conducted an online study with 830 participants from the U.S., aiming to investigate user perceptions towards continuous authentication across different classes of online services. Our analysis identified four groups of biometric traits that directly reflect users' willingness to disclose them. Our findings demonstrate that willingness to disclose is influenced by both the specific biometric traits and the type of online service involved. User perceptions are strongly shaped by factors such as response efficacy, perceived privacy risks associated with the biometric traits, and concerns about the service providers' handling of such data. Our results emphasize the inadequacy of one-size-fits-all solutions and provide valuable insights for the design and implementation of continuous authentication systems.
