Biblio

Any technique to ensure memory safety requires knowledge of (a) precise array bounds and (b) the data types accessed by memory load/store and pointer move instructions (called, owners) in the program. While this information can be effectively derived by compiler-level approaches much of this information may be lost during the compilation process and become unavailable to binary-level tools. In this work we conduct the first detailed study on how accurately can this information be extracted or reconstructed by current state-of-the-art static reverse engineering (RE) platforms for binaries compiled with and without debug symbol information. Furthermore, it is also unclear how the imprecision in array bounds and instruction owner information that is obtained by the RE tools impacts the ability of techniques to detect illegal memory accesses at run-time. We study this issue by designing, building, and deploying a novel binary-level technique to assess the properties and effectiveness of the information provided by the static RE algorithms in the first stage to guide the run-time instrumentation to detect illegal memory accesses in the decoupled second stage. Our work explores the limitations and challenges for static binary analysis tools to develop accurate binary-level techniques to detect memory errors.

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.