Third Party Tracking in the Mobile Ecosystem

Third party tracking allows companies to identify users and track their behaviour across multiple digital services. This paper presents an empirical study of the prevalence of third-party trackers on 959,000 apps from the US and UK Google Play stores. We find that most apps contain third party tracking, and the distribution of trackers is long-tailed with several highly dominant trackers accounting for a large portion of the coverage. The extent of tracking also differs between categories of apps; in particular, news apps and apps targeted at children appear to be amongst the worst in terms of the number of third party trackers associated with them. Third party tracking is also revealed to be a highly trans-national phenomenon, with many trackers operating in jurisdictions outside the EU. Based on these findings, we draw out some significant legal compliance challenges facing the tracking industry.Comment: Corrected missing company info (Linkedin owned by Microsoft). Figures for Microsoft and Linkedin re-calculated and added to Table

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations

Zynga’s FarmVille, social games, and the ethics of big data mining

The increasing necessity of engaging in social interaction through online commercial providers such as Facebook, alongside the ability of providers to extract, aggregate, analyse, and commercialise the data and metadata such activities produce, have attracted considerable attention amongst the media and academic commentators alike. While much of the attention has been focused on the data mining of social networking services such as Facebook, it is equally important to recognise the widespread adoption of large-scale data mining practices in a number of realms, including social games such as the well-known FarmVille and its sequels, created by Zynga. The implicit contract that the public who use these services necessarily engage in requires them to trade information about their friends, their likes, their desires, and their consumption habits in return for their participation in the service. This paper will critically explore the realm of social games utilising Zynga as a central example, with a view to examine the practices, politics, and ethics of data mining and the inherent social media contradiction. In determining whether this contradiction is accidental or purposeful, this paper will ask, in effect, whether Zynga and other big data miners behind social games are entrepreneurial heroes, more sinister FarmVillains, or whether it is possible at all to draw a line between the two? In doing so, Zynga’s data mining approach and philosophy provide an important indicator about the broader integration of data analytics into a range of everyday activities

Towards location privacy awareness on geo-social networks

With the current trend of embedding location services within social networks, an ever growing amount of users' spatiotemporal tracks are being collected and used to generate user profiles. Issues of personal privacy and especially those stemming from tracking user location become more important to address. In this work, it is argued that support of location privacy awareness within social networks is needed to maintain the users' trust in their services. Current practices of pre-configuring location disclosure settings have been shown to be limited, where users' sense of location privacy dynamically change with context. In this paper, location privacy awareness is considered within a composite view of place, time and social data recorded in user profiles. The paper examines the possible threats to personal privacy from exposure of this data and the design of feedback tools to allow users to control their privacy. A user study is used to examine the impact of the feedback provided on users' perception of privacy and the link between their privacy concerns and their attitude towards using the geo-social network. Findings confirm the strong need for more transparent access to and control over user location profiles, and guide the proposal of recommendations to the design of more privacy-sensitive geo-social networks

Are my Apps Peeking? Comparing Nudging Mechanisms to Raise Awareness of Access to Mobile Front-facing Camera

Mobile applications that are granted permission to access the device’s camera can access it at any time without necessarily showing the camera feed to the user or communicating that it is being used. This lack of transparency raises privacy concerns, which are exacerbated by the increased adoption of applications that leverage front-facing cameras. Through a focus group we identified three promising approaches for nudging the user that the camera is being accessed, namely: notification bar, frame, and camera preview. We experimented with accompanying each nudging method with vibrotactile and audio feedback. Results from a user study (N=15) show that while using frame nudges is the least annoying and interrupting, but was less understandable than the camera feed and notifications. On the other hand, participants found that indicating camera usage by showing its feed or by using notifications is easy to understand. We discuss how these nudges raise user awareness and the effects on app usage and perception

A holistic framework for enhancing privacy awareness

Home users face increasingly higher risks of privacy loss and struggle with the difficult task of protecting large volumes of personal information. Most privacy research assumes that users have uniform privacy requirements. The main problem with this approach is that research has also shown that users have different privacy attitudes and expectations based upon a variety of factors, including (but not limited to) gender, age and education level. Privacy therefore can mean different things in different contexts, to different people at different times. For example, some uses are less concerned regarding the sharing and use of their location information while others will be very concerned. Therefore, it is important to factor these requirements in to a privacy-awareness model that can enhance user\u27s awareness and make more informed decisions to reduce their specific degree of exposure. The quantity and range of sensitive information also requires approaches that give users back the control over their data. Therefore, prioritization of privacy-related information based on an individual user basis should be utilised to ensure relevant and timely notification about privacy-related information that is important to the user. This paper presents a critical analysis of the current state of the art and proposes a novel mobile-based architecture to provide users with effective and usable privacy protection

A holistic framework for enhancing privacy awareness

Home users face increasingly higher risks of privacy loss and struggle with the difficult task of protecting large volumes of personal information. Most privacy research assumes that users have uniform privacy requirements. The main problem with this approach is that research has also shown that users have different privacy attitudes and expectations based upon a variety of factors, including (but not limited to) gender, age and education level. Privacy therefore can mean different things in different contexts, to different people at different times. For example, some uses are less concerned regarding the sharing and use of their location information while others will be very concerned. Therefore, it is important to factor these requirements in to a privacy-awareness model that can enhance user\u27s awareness and make more informed decisions to reduce their specific degree of exposure. The quantity and range of sensitive information also requires approaches that give users back the control over their data. Therefore, prioritization of privacy-related information based on an individual user basis should be utilised to ensure relevant and timely notification about privacy-related information that is important to the user. This paper presents a critical analysis of the current state of the art and proposes a novel mobile-based architecture to provide users with effective and usable privacy protection

Does This App Respect My Privacy? Design and Evaluation of Information Materials Supporting Privacy-Related Decisions of Smartphone Users

Over the years, the wide-spread usage of smartphones leads to large amounts of personal data being stored by them. These data, in turn, can be accessed by the apps installed on the smartphones, and potentially misused, jeopardizing the privacy of smartphone users. While the app stores provide indicators that allow an estimation of the privacy risks of individual apps, these indicators have repeatedly been shown as too confusing for the lay users without technical expertise. We have developed an information flyer with the goal of providing decision support for these users and enabling them make more informed decisions regarding their privacy upon choosing and installing smartphone apps. Our flyer is based on previous research in mental models of smartphone privacy and security and includes heuristics for choosing privacy-friendlier apps used by IT-Security experts. It also addresses common misconceptions of users regarding smartphones. The flyer was evaluated in a user study. The results of the study show, that the users who read the flyer tend to take privacy-relevant factors into account by relying on the heuristics in the flyer more often. Hence, the flyer succeeds in supporting users in making more informed privacy-related decisions