Android Permissions Remystified: A Field Study on Contextual Integrity

Due to the amount of data that smartphone applications can potentially access, platforms enforce permission systems that allow users to regulate how applications access protected resources. If users are asked to make security decisions too frequently and in benign situations, they may become habituated and approve all future requests without regard for the consequences. If they are asked to make too few security decisions, they may become concerned that the platform is revealing too much sensitive information. To explore this tradeoff, we instrumented the Android platform to collect data regarding how often and under what circumstances smartphone applications are accessing protected resources regulated by permissions. We performed a 36-person field study to explore the notion of "contextual integrity," that is, how often are applications accessing protected resources when users are not expecting it? Based on our collection of 27 million data points and exit interviews with participants, we examine the situations in which users would like the ability to deny applications access to protected resources. We found out that at least 80% of our participants would have preferred to prevent at least one permission request, and overall, they thought that over a third of requests were invasive and desired a mechanism to block them

The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

`I make up a silly name': Understanding Children's Perception of Privacy Risks Online

Children under 11 are often regarded as too young to comprehend the implications of online privacy. Perhaps as a result, little research has focused on younger kids' risk recognition and coping. Such knowledge is, however, critical for designing efficient safeguarding mechanisms for this age group. Through 12 focus group studies with 29 children aged 6-10 from UK schools, we examined how children described privacy risks related to their use of tablet computers and what information was used by them to identify threats. We found that children could identify and articulate certain privacy risks well, such as information oversharing or revealing real identities online; however, they had less awareness with respect to other risks, such as online tracking or game promotions. Our findings offer promising directions for supporting children's awareness of cyber risks and the ability to protect themselves online.Comment: 13 pages, 1 figur

Contextual permission models for better privacy protection

Despite corporate cyber intrusions attracting all the attention, privacy breaches that we, as ordinary users, should be worried about occur every day without any scrutiny. Smartphones, a household item, have inadvertently become a major enabler of privacy breaches. Smartphone platforms use permission systems to regulate access to sensitive resources. These permission systems, however, lack the ability to understand users' privacy expectations leaving a significant gap between how permission models behave and how users would want the platform to protect their sensitive data. This dissertation provides an in-depth analysis of how users make privacy decisions in the context of Smartphones and how platforms can accommodate user's privacy requirements systematically. We first performed a 36-person field study to quantify how often applications access protected resources when users are not expecting it. We found that when the application requesting the permission is running invisibly to the user, they are more likely to deny applications access to protected resources. At least 80% of our participants would have preferred to prevent at least one permission request. To explore the feasibility of predicting user's privacy decisions based on their past decisions, we performed a longitudinal 131-person field study. Based on the data, we built a classifier to make privacy decisions on the user's behalf by detecting when the context has changed and inferring privacy preferences based on the user's past decisions. We showed that our approach can accurately predict users' privacy decisions 96.8% of the time, which is an 80% reduction in error rate compared to current systems. Based on these findings, we developed a custom Android version with a contextually aware permission model. The new model guards resources based on user's past decisions under similar contextual circumstances. We performed a 38-person field study to measure the efficiency and usability of the new permission model. Based on exit interviews and 5M data points, we found that the new system is effective in reducing the potential violations by 75%. Despite being significantly more restrictive over the default permission systems, participants did not find the new model to cause any usability issues in terms of application functionality.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat

Scalable Database Management System (DBMS) architecture with Innesto

Database Management systems (DBMS) have been in the core of Information Systems for decades and their importance is getting higher and higher with current high growth in user demand and rising necessity to handle big data. With recent emergence of new style of deployments in the cloud, decades old architectures in DBMS have been greatly challenged due to their inability to scale beyond single computing node and to handle big data. This new requirement has spawned new directions along scaling data storage architectures. Most of the work surfaced lacks the applicability across many domains as they were targeting only a specific domain. We present a novel scalable architecture which is implemented using a distributed spatial partitioning tree (SPT). This new architecture replaces only the storage layer of a conventional DBMS thus leaving its applicability across domains intact and provides strict consistency and isolation. Indexing and locking are two important components of a Relational Database Management System (DBMS) which pose as potential bottleneck when scaling. Our new approach based on SPT provides a novel scalable alternative for these components. Our evaluations using the TPC-C workload show they are capable of scaling beyond single computing node and support more concurrent users compared to a single node conventional system. We believe our contributions to be an important first step towards the goal of a scalable, cloud aware and full-featured DBMS as a service.Science, Faculty ofComputer Science, Department ofGraduat

Privacy Attitudes of Smart Speaker Users

As devices with always-on microphones located in people’s homes, smart speakers have significant privacy implications. We surveyed smart speaker owners about their beliefs, attitudes, and concerns about the recordings that are made and shared by their devices. To ground participants’ responses in concrete interactions, rather than collecting their opinions abstractly, we framed our survey around randomly selected recordings of saved interactions with their devices. We surveyed 116 owners of Amazon and Google smart speakers and found that almost half did not know that their recordings were being permanently stored and that they could review them; only a quarter reported reviewing interactions, and very few had ever deleted any. While participants did not consider their own recordings especially sensitive, they were more protective of others’ recordings (such as children and guests) and were strongly opposed to use of their data by third parties or for advertising. They also considered permanent retention, the status quo, unsatisfactory. Based on our findings, we make recommendations for more agreeable data retention policies and future privacy controls