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Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA's high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users. To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication against account takeover attacks. Our study on 65 participants shows that users find RBA more usable than two-factor authentication equivalents and more secure than password-only authentication. We identify pitfalls and provide guidelines for putting RBA into practice.
Risk-based authentication (RBA) aims to strengthen password-based authentication rather than replacing it. RBA does this by monitoring and recording additional features during the login process. If feature values at login time differ significantly from those observed before, RBA requests an additional proof of identification. Although RBA is recommended in the NIST digital identity guidelines, it has so far been used almost exclusively by major online services. This is partly due to a lack of open knowledge and implementations that would allow any service provider to roll out RBA protection to its users.
To close this gap, we provide a first in-depth analysis of RBA characteristics in a practical deployment. We observed N=780 users with 247 unique features on a real-world online service for over 1.8 years. Based on our collected data set, we provide (i) a behavior analysis of two RBA implementations that were apparently used by major online services in the wild, (ii) a benchmark of the features to extract a subset that is most suitable for RBA use, (iii) a new feature that has not been used in RBA before, and (iv) factors which have a significant effect on RBA performance. Our results show that RBA needs to be carefully tailored to each online service, as even small configuration adjustments can greatly impact RBA's security and usability properties. We provide insights on the selection of features, their weightings, and the risk classification in order to benefit from RBA after a minimum number of login attempts.
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.
Voice assistants (VA) collect data about users’ daily life including interactions with other connected devices, musical preferences, and unintended interactions. While users appreciate the convenience of VAs, their understanding and expectations of data collection by vendors are often vague and incomplete. By making the collected data explorable for consumers, our research-through-design approach seeks to unveil design resources for fostering data literacy and help users in making better informed decisions regarding their use of VAs. In this paper, we present the design of an interactive prototype that visualizes the conversations with VAs on a timeline and provides end users with basic means to engage with data, for instance allowing for filtering and categorization. Based on an evaluation with eleven households, our paper provides insights on how users reflect upon their data trails and presents design guidelines for supporting data literacy of consumers in the context of VAs.
Most people use disaster apps infrequently, primarily only in situations of turmoil, when they are physically or emotionally vulnerable. Personal data may be necessary to help them, data protections may be waived. In some circumstances, free movement and liberties may be curtailed for public protection, as was seen in the current COVID pandemic. Consuming and producing disaster data can deepen problems arising at the confluence of surveillance and disaster capitalism, where data has become a tool for solutionist instrumentarian power (Zuboff 2019, Klein 2008) and part of a destructive mode of one world worlding (Law 2015, Escobar 2020). The special use of disaster apps prompts us to ask what role consumer protection could play in safeguarding democratic liberties. Within this work, a set of current approaches are briefly reviewed and two case studies are presented of what we call appropriation or design against datafication. These combine document analysis and literature research with several months of online and field ethnographic observation. The first case study examines disaster app use in response to the 2010 Haiti earthquake, the second explores COVID Contact Tracing in Taiwan in 2020/21. Against this backdrop we ask, ‘how could and how should consumer protection respond to problems of surveillance disaster capitalism?’ Drawing on our work with the is IT ethical? Exchange, a co-designed community platform and knowledge exchange for disaster information sharing, and a Societal Readiness Assessment Framework that we are developing alongside it, we explore how co-design methodologies could help define answers.
Sharing economies enabled by technical platforms have been studied regarding their economic, legal, and social effects, as well as with regard to their possible influences on CSCW topics such as work, collaboration, and trust. While a lot current research is focusing on the sharing economy and related communities, there is little work addressing the phenomenon from a socio-technical point of view. Our workshop is meant to address this gap. Building on research themes and discussion from last year’s ECSCW, we seek to engage deeper with topics such as novel socio-technical approaches for enabling sharing communities, discussing issues around digital consumer and worker protection, as well as emerging challenges and opportunities of existing platforms and approaches.
Data emerged as a central success factor for companies to benefit from digitization. However, the skills in successfully creating value from data – especially at the management level – are not always profound. To address this problem, several canvas models have already been designed. Canvas models are usually created to write down an idea in a structured way to promote transparency and traceability. However, some existing data science canvas models mainly address developers and are thus unsuitable for decision-makers and communication within interdisciplinary teams. Based on a literature review, we identified influencing factors that are essential for the success of data science projects. With the information gained, the Data Science Canvas was developed in an expert workshop and finally evaluated by practitioners to find out whether such an instrument could support data-driven value creation.
Due to ongoing digitalization, more and more cloud services are finding their way into companies. In this context, data integration from the various software solutions, which are provided both on-premise (local use or licensing for local use of software) and as a service, is of great importance. In this regard, Integration Platform as a Service (IPaaS) models aim to support companies as well as software providers in the context of data integration by providing connectors to enable data flow between different applications and systems and other integration services. Since previous research has mostly focused on technical or legal aspects of IPaaS, this article focuses on deriving integration practices and design-related barriers and drivers regarding the adoption of IPaaS. Therefore, we conducted 10 interviews with experts from different software as a services vendors. Our results show that the main factors regarding the adoption of IPaaS are the standardization of data models, the usability and variety of connectors provided, and the issues regarding data privacy, security, and transparency.
Experience made with free and open source software (FOSS) in the public research is shared with the community. The motivation for using and publishing FOSS is to increase visibility, transparancy and feedback quality while at the same time lowering software licensing costs. Also, the idea of giving back and returning a value plays a role. The most frequently given counter arguments are discussed. In the end, it’s important to embed FOSS publishing into the company’s strategy for the exploitation of scientific research results. To help with this, a checklist of criteria to indicate FOSS publishing is suggested. On the backround of wireless sensor networks, some case studies of FOSS contribution are detailed. The emphasis is on checking the original motivation and the spirit of FOSS back with the reality. Finally, further potential of publishing FOSS in the context of scientific research is identified.
This paper gives an overview of how we can benefit from using container technology in our academic work. It aims to be a starting point for fellow researchers which also think about applying these technologies. Hence, we focus on decribing our own experiences and motivations instead of proving hard scientific facts.
Critical consumerism is complex as ethical values are difficult to negotiate, appropriate products are hard to find, and product information is overwhelming. Although recommender systems offer solutions to reduce such complexity, current designs are not appropriate for niche practices and use non-personalized intransparent ethics. To support critical consumption, we conducted a design case study on a personalized food recommender system. Therefore, we first conducted an empirical pre-study with 24 consumers to understand value negotiations and current practices, co-designed the recommender system, and finally evaluated it in a real-world trial with ten consumers. Our findings show how recommender systems can support the negotiation of ethical values within the context of consumption practices, reduce the complexity of finding products and stores, and strengthen consumers. In addition to providing implications for the design to support critical consumption practices, we critically reflect on the scope of such recommender systems and its appropriation.
Recent publications propose concepts of systems that integrate the various services and data sources of everyday food practices. However, this research does not go beyond the conceptualization of such systems. Therefore, there is a deficit in understanding how to combine different services and data sources and which design challenges arise from building integrated Household Information Systems. In this paper, we probed the design of an Integrated Household Information System with 13 participants. The results point towards more personalization, automatization of storage administration and enabling flexible artifact ecologies. Our paper contributes to understanding the design and usage of Integrated Household Information Systems, as a new class of information systems for HCI research.
With the debates on climate change and sustainability, a reduction of the share of cars in the modal split has become increasingly prevalent in both public and academic discourse. Besides some motivational approaches, there is a lack of ICT artifacts that successfully raise the ability of consumers to adopt sustainable mobility patterns. To further understand the requirements and the design of these artifacts within everyday mobility adopted a practice-lens. This lens is helpful to get a broader perspective on the use of ICT artifacts along consumers’ transformational journey towards sustainable mobility practices. Based on 12 retrospective interviews with car-free mobility consumers, we argue that artifacts should not be viewed as ’magic-bullet’ solutions but should accompany the complex transformation of practices in multifaceted ways. Moreover, we highlight in particular the difficulties of appropriating shared infrastructures and aligning own practices with them. This opens up a design space to provide more support for these kinds of material-interactions, to provide access to consumption infrastructures and make them usable, rather than leaving consumers alone with increased motivation.
Threats to passwords are still very relevant due to attacks like phishing or credential stuffing. One way to solve this problem is to remove passwords completely. User studies on passwordless FIDO2 authentication using security tokens demonstrated the potential to replace passwords. However, widespread acceptance of FIDO2 depends, among other things, on how user accounts can be recovered when the security token becomes permanently unavailable. For this reason, we provide a heuristic evaluation of 12 account recovery mechanisms regarding their properties for FIDO2 passwordless authentication. Our results show that the currently used methods have many drawbacks. Some even rely on passwords, taking passwordless authentication ad absurdum. Still, our evaluation identifies promising account recovery solutions and provides recommendations for further studies.
New cars are increasingly "connected" by default. Since not having a car is not an option for many people, understanding the privacy implications of driving connected cars and using their data-based services is an even more pressing issue than for expendable consumer products. While risk-based approaches to privacy are well established in law, they have only begun to gain traction in HCI. These approaches are understood not only to increase acceptance but also to help consumers make choices that meet their needs. To the best of our knowledge, perceived risks in the context of connected cars have not been studied before. To address this gap, our study reports on the analysis of a survey with 18 open-ended questions distributed to 1,000 households in a medium-sized German city. Our findings provide qualitative insights into existing attitudes and use cases of connected car features and, most importantly, a list of perceived risks themselves. Taking the perspective of consumers, we argue that these can help inform consumers about data use in connected cars in a user-friendly way. Finally, we show how these risks fit into and extend existing risk taxonomies from other contexts with a stronger social perspective on risks of data use.
XML Signature Wrapping (XSW) has been a relevant threat to web services for 15 years until today. Using the Personal Health Record (PHR), which is currently under development in Germany, we investigate a current SOAP-based web services system as a case study. In doing so, we highlight several deficiencies in defending against XSW. Using this real-world contemporary example as motivation, we introduce a guideline for more secure XML signature processing that provides practitioners with easier access to the effective countermeasures identified in the current state of research.