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New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet the minimal requirements that we set based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22--23 percentage points; and providing more granular controls on the first page decreases consent by 8--20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance. 

This paper explores how individuals' privacy-related decision-making processes may be influenced by their pre-existing relationships to companies in a wider social and economic context. Through an online role-playing exercise, we explore attitudes to a range of services including home automation, Internet-of-Things and financial services. We find that individuals do not only consider the privacy-related attributes of applications, devices or services in the abstract. Rather, their decisions are heavily influenced by their pre-existing perceptions of, and relationships with, the companies behind such apps, devices and services. In particular, perceptions about a company's size, level of regulatory scrutiny, relationships with third parties, and pre-existing data exposure lead some users to choose an option which might otherwise appear worse from a privacy perspective. This finding suggests a need for tools that support users to incorporate these existing perceptions and relationships into their privacy-related decision making. 

Most users of smartphone apps remain unaware of what data about them is being collected, by whom, and how these data are being used. In this mixed methods investigation, we examine the question of whether revealing key data collection practices of smartphone apps may help people make more informed privacyrelated decisions. To investigate this question, we designed and prototyped a new class of privacy indicators, called Data Controller Indicators (DCIs), that expose previously hidden information flows out of the apps. Our lab study of DCIs suggests that such indicators do support people in making more confident and consistent choices, informed by a more diverse range of factors, including the number and nature of third-party companies that access users’ data. Furthermore, personalised DCIs, which are contextualised against the other apps an individual already uses, enable them to reason effectively about the differential impacts on their overall information exposure.