The cloud computing paradigm, which involves distributed computation on multiple large-scale datasets, will become successful only if it ensures privacy, confidentiality, and integrity for the data belonging to individuals and organizations. We present Airavat, a novel integration of decentralized information flow control (DIFC) and differential privacy that provides strong security and privacy guarantees for MapReduce computations. Airavat allows users to use arbitrary mappers, prevents unauthorized leakage of sensitive data during the computation, and supports automatic declassification of the results when the latter do not violate individual privacy. Airavat minimizes the amount of trusted code in the system and allows users without security expertise to perform privacy-preserving computations on sensitive data. Our prototype implementation demonstrates the flexibility of Airavat on a wide variety of case studies. The prototype is efficient, with run-times on Amazon’s cloud computing infrastructure within 25 % of a MapReduce system with no security.

Despite the demonstrated usefulness of dynamic data flow tracking (DDFT) techniques in a variety of security applications, the poor performance achieved by available prototypes prevents their widespread adoption and use in production systems. We present and evaluate a novel methodology for improving the performance overhead of DDFT frameworks, by combining static and dynamic analysis. Our intuition is to separate the program logic from the corresponding tracking logic, extracting the semantics of the latter and abstracting them using a Taint Flow Algebra. We then apply optimization techniques to eliminate redundant tracking logic and minimize interference with the target program. Our optimizations are directly applicable to binary-only software and do not require any high level semantics. Furthermore, they do not require additional resources to improve performance, neither do they restrict or remove functionality. Most importantly, our approach is orthogonal to optimizations devised in the past, and can deliver additive performance benefits. We extensively evaluate the correctness and impact of our optimizations, by augmenting a freely available high-performance DDFT framework, and applying it to multiple applications, including command line utilities, server applications, language runtimes, and web browsers. Our results show a speedup of DDFT by as much as 2.23×, with an average of 1.72 × across all tested applications. 1

Mobile code is now a nearly inescapable component of modern computing, thanks to client-side code that runs within web browsers. The usual tension between security and functionality is particularly acute in a mobile-code setting, and current platforms disappoint on both dimensions. We introduce a new architecture for secure mobile code, with which developers can use, publish, and share mobile code securely across trust domains. This architecture enables new kinds of distributed applications, and makes it easier to reuse and evolve code from untrusted providers. The architecture gives mobile code considerable expressive power: it can securely access distributed, persistent, shared information from multiple trust domains, unlike web applications bound by the same-origin policy. The core of our approach is analyzing how flows of information within mobile code affect confidentiality and integrity. Because mobile code is untrusted, this analysis requires novel constraints on information flow and authority. We show that these constraints offer principled enforcement of strong security while avoiding the limitations of current mobile-code security mechanisms. We evaluate our approach by demonstrating a variety of mobilecode applications, showing that new functionality can be offered along with strong security. 1.

Private and confidential information is increasingly stored online and increasingly being exposed due to human errors as well as malicious attacks. Information leaks threaten confidentiality, lead to lawsuits, damage enterprise reputations, and cost billion of dollars. While distributed computing architectures provide data and service integration, they also create information flow control problems due to the interaction complexity among service providers. A main problem is the lack of an appropriate programming model to capture expected information flow behaviors in these large distributed software infrastructures. This research tackles this problem by proposing a programming methodology and enforcement platform for application developers to protect and share their sensitive data. We introduce Aeolus, a new platform intended to make it easier to build distributed

The cloud computing paradigm, which involves distributed computation on multiple large-scale datasets, will become successful only if it ensures privacy, confidentiality, and integrity for the data belonging to individuals and organizations. We present Airavat, a novel integration of decentralized information flow control (DIFC) and differential privacy that provides strong security and privacy guarantees for MapReduce computations. Airavat allows users to use arbitrary mappers, prevents unauthorized leakage of sensitive data during the computation, and supports automatic declassification of the results when the latter do not violate individual privacy. Airavat minimizes the amount of trusted code in the system and allows users without security expertise to perform privacy-preserving computations on sensitive data. Our prototype implementation demonstrates the flexibility of Airavat on a wide variety of case studies. The prototype is efficient, with run-times on Amazon’s cloud computing infrastructure within 25 % of a MapReduce system with no security. 1

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Today’s smartphone operating systems fail to provide users with adequate control and visibility into how third-party applications use their private data. We present TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system for the popular Android platform that can simultaneously track multiple sources of sensitive data. TaintDroid’s efficiency to perform real-time analysis stems from its novel system design that leverages the mobile platform’s virtualized system architecture. TaintDroid incurs only 14 % performance overhead on a CPU-bound micro-benchmark with little, if any, perceivable overhead when running thirdparty applications. We use TaintDroid to study the behavior of 30 popular third-party Android applications and find several instances of misuse of users ’ private information. We believe that TaintDroid is the first working prototype demonstrating that dynamic taint tracking and analysis provides informed use of third-party applications in existing smartphone operating systems. 1

Today’s smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. We address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. TaintDroid provides realtime analysis by leveraging Android’s virtualized execution environment. TaintDroid incurs only 14 % performance overhead on a CPU-bound micro-benchmark and imposes negligible overhead on interactive third-party applications. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users ’ private information across 20 applications. Monitoring sensitive data with TaintDroid provides informed use of third-party applications for phone users and valuable input for smartphone security service firms seeking to identify misbehaving applications. 1

We present Airavat, a MapReduce-based system which provides strong security and privacy guarantees for distributed computations on sensitive data. Airavat is a novel integration of mandatory access control and differential privacy. Data providers control the security policy for their sensitive data, including a mathematical bound on potential privacy violations. Users without security expertise can perform computations on the data, but Airavat confines these computations, preventing information leakage beyond the data provider’s policy. Our prototype implementation demonstrates the flexibility of Airavat on several case studies. The prototype is efficient, with run times on Amazon’s cloud computing infrastructure within 32 % of a MapReduce system with no security. 1

I am excited by the challenge of making software significantly more reliable, scalable, and secure than it is today and thus helping achieve advances in areas such as science, education, health, and energy. I have focused on the problem of software bugs, which cause errors that cost billions of dollars annually and sometimes result in injury or death. These bugs are pervasive in modern software, which is only becoming more complex as developers add features, integrate components, and write concurrent software. State-of-the-art testing, static analysis, and modern language features eliminate some but not all bugs. In particular, thorough in-house testing cannot test all possible environments, configurations, and thread interleavings. Deployed software thus contains bugs that are hard to reproduce, find, and fix. Deployed systems need support for improving reliability in order to achieve highly robust software. My interests lie in solving these problems with programming languages and runtime systems. I focus on building analyses and systems that help developers and users make software more reliable and effective. I am particularly interested in approaches that are lightweight and flexible enough to run alongside deployed systems and that provide rigorous guarantees about accuracy and performance. My dissertation work introduces broadly applicable techniques that help developers find and fix bugs in deployed systems and help users by automatically tolerating the effects of errors. Two significant contributions are (1) efficient techniques that add context sensitivity to a broad set of analyses for reliability and security (first section below), and (2) an automatic approach for minimizing the effects of programmers’