In this paper we present a methodology to analyze users' concerns and perspectives about privacy at scale. We leverage NLP techniques to process millions of mobile app reviews and extract privacy concerns. Our methodology is composed of a binary classifier that distinguishes between privacy and non-privacy related reviews. We use clustering to gather reviews that discuss similar privacy concerns, and employ summarization metrics to extract representative reviews to summarize each cluster. We apply our methods on 287M reviews for about 2M apps across the 29 categories in Google Play to identify top privacy pain points in mobile apps. We identified approximately 440K privacy related reviews. We find that privacy related reviews occur in all 29 categories, with some issues arising across numerous app categories and other issues only surfacing in a small set of app categories. We show empirical evidence that confirms dominant privacy themes - concerns about apps requesting unnecessary permissions, collection of personal information, frustration with privacy controls, tracking and the selling of personal data. As far as we know, this is the first large scale analysis to confirm these findings based on hundreds of thousands of user inputs. We also observe some unexpected findings such as users warning each other not to install an app due to privacy issues, users uninstalling apps due to privacy reasons, as well as positive reviews that reward developers for privacy friendly apps. Finally we discuss the implications of our method and findings for developers and app stores.

String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code. In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing. We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.

Research has established the wide variety of security failures in mobile apps, their consequences, and how app developers introduce or exacerbate them. What is not well known is why developers do so---what is the rationale underpinning the decisions they make which eventually strengthen or weaken app security? This is all the more complicated in modern app development's increasingly diverse demographic: growing numbers of independent, solo, or small team developers who do not have the organizational structures and support that larger software development houses enjoy.

Cyber security decision making is inherently complicated, with nearly every decision having knock-on consequences for an organisation’s vulnerability and exposure. This is further compounded by the fact that decision-making actors are rarely security experts and may have an incomplete understanding of the security that the organisation currently has in place. They must contend with a multitude of possible security options that they may only partially understand. This challenge is met by decision makers’ risk thinking —their strategies for identifying risks, assessing their severity, and prioritising responses. We study the risk thinking strategies employed by teams of participants in an existing dataset derived from a tabletop cyber-physical systems security game. Our analysis identifies four structural patterns of risk thinking and two reasoning strategies: risk-first and opportunity-first . Our work highlights that risk-first approaches (as prescribed by the likes of NIST-800-53 and ISO 27001) are followed neither substantially nor exclusively when it comes to decision making. Instead, our analysis finds that decision making is affected by the plasticity of teams—that is, the ability to readily switch between ideas and practising both risk-first and opportunity-first reasoning.

Users of mobile apps sometimes express discomfort or concerns with what they see as unnecessary or intrusive permission requests by certain apps. However encouraging mobile app developers to request fewer permissions is challenging because there are many reasons why permissions are requested; furthermore, prior work [25] has shown it is hard to disambiguate the purpose of a particular permission with high certainty.

Stakeholders' security decisions play a fundamental role in determining security requirements, yet, little is currently understood about how different stakeholder groups within an organisation approach security and the drivers and tacit biases underpinning their decisions. We studied and contrasted the security decisions of three demographics-security experts, computer scientists and managers-when playing a tabletop game that we designed and developed. The game tasks players with managing the security of a cyber-physical environment while facing various threats. Analysis of 12 groups of players (4 groups in each of our demographics) reveals strategies that repeat in particular demographics, e.g., managers and security experts generally favoring technological solutions over personnel training, which computer scientists preferred. Surprisingly, security experts were not ipso facto better players-in some cases, they made very questionable decisions-yet they showed a higher level of confidence in themselves. We classified players' decision-making processes, i.e., procedure-, experience-, scenario- or intuition-driven. We identified decision patterns, both good practices and typical errors and pitfalls. Our game provides a requirements sandbox in which players can experiment with security risks, learn about decision-making and its consequences, and reflect on their own perception of security.

Motivation: The security of any system is a direct consequence of stakeholders' decisions regarding security requirements. Such decisions are taken with varying degrees of expertise, and little is currently understood about how various demographics - security experts, general computer scientists, managers - approach security decisions and the strategies that underpin those decisions. What are the typical decision patterns, the consequences of such patterns and their impact on the security of the system in question? Nor is there any substantial understanding of how the strategies and decision patterns of these different groups contrast. Is security expertise necessarily an advantage when making security decisions in a given context? Answers to these questions are key to understanding the "how" and "why" behind security decision processes.

There will be an estimated 35 zettabytes (35 × 1021) of digital records worldwide by the year 2020. This effectively amounts to privacy management on an ultra-large-scale. In this briefing, we discuss the privacy challenges posed by such an ultra-large-scale ecosystem - we term this “Privacy in the Large”. We will contrast existing approaches to privacy management, reflect on their strengths and limitations in this regard and outline key software engineering research and practice challenges to be addressed in the future.