Events

Today computers store and analyze valuable and sensitive data. As a result we need to protect this data against confidentiality and integrity violations that can result in the illicit release, loss, or modification of a user’s and an organization’s sensitive data such as personal media content or client records. Existing techniques protecting confidentiality and integrity lack either efficiency or are vulnerable to malicious attacks. In this thesis we suggest techniques, Guardat and ERIM, to efficiently and robustly protect persistent and in-memory data. To protect the confidentiality and integrity of persistent data, clients specify per-file policies to Guardat declaratively, concisely and separately from code. Guardat enforces policies by mediating I/O in the storage layer. In contrast to prior techniques, we protect against accidental or malicious circumvention of higher software layers. We present the design and prototype implementation, and demonstrate that Guardat efficiently enforces example policies in a web server. To protect the confidentiality and integrity of in-memory data, ERIM isolates sensitive data using Intel Memory Protection Keys (MPK), a recent x86 extension to partition the address space. However, MPK does not protect against malicious attacks by itself. We prevent malicious attacks by combining MPK with call gates to trusted entry points and ahead-of-time binary inspection. In contrast to existing techniques, ERIM efficiently protects frequently-used session keys of web servers, an in-memory reference monitor’s private state, and managed runtimes from native libraries. These use cases result in high switch rates of the order of 10 5 –10 6 switches/s. Our experiments demonstrate less then 1% runtime overhead per 100,000 switches/s, thus outperforming existing techniques.

Much of the research in machine learning has focused on designing algorithms for discovering knowledge from data and learning a target model. What if there is a ``teacher" who knows the target already, and wants to steer the ``learner" towards this target as quickly as possible?  For instance, in an educational setting, the teacher (e.g., a tutoring system) has an educational goal that she wants to communicate to a student via a set of demonstrations and lessons; in adversarial attacks known as training-set poisoning, the teacher (e.g., a hacking algorithm) manipulates the behavior of a machine learning system by maliciously modifying the training data. This lecture will provide an overview of machine teaching and cover the following three aspects: (i) how machine teaching differs from machine learning, (ii) highlight the problem space of machine teaching across different dimensions, and (iii) discuss our recent work on developing teaching algorithms for human learners.

Symbolic execution is a program analysis technique that can automatically explore and analyse paths through a program. While symbolic execution was initially introduced in the seventies, it has only received significant attention during the last decade, due to tremendous advances in constraint solving technology and effective blending of symbolic and concrete execution into what is often called dynamic symbolic execution. Dynamic symbolic execution is now a key ingredient in many computer science areas, such as software engineering, computer security, and software systems, to name just a few. In this talk, I will discuss recent advances and ongoing challenges in the area of dynamic symbolic execution, drawing upon our experience developing several symbolic execution tools for many different scenarios, such as high-coverage test input generation, bug and security vulnerability detection, patch testing and bounded verification, among many others.

The rise of algorithmic decision making in a variety of applications has also raised concerns about its potential for discrimination against certain social groups. However, incorporating nondiscrimination goals into the design of algorithmic decision making systems (or, classifiers) has proven to be quite challenging. These challenges arise mainly due to the computational complexities involved in the process, and the inadequacy of existing measures to computationally capture discrimination in various situations. The goal of this thesis is to tackle these problems.

First, with the aim of incorporating existing measures of discrimination (namely, disparate treatment and disparate impact) into the design of well-known classifiers, we introduce a mechanism of decision boundary covariance, that can be included in the formulation of any convex boundary-based classifier in the form of convex constraints. Second, we propose alternative measures of discrimination. Our first proposed measure, disparate mistreatment, is useful in situations when unbiased ground truth training data is available. The other two measures, preferred treatment and preferred impact, are useful in situations when feature and class distributions of different social groups are significantly different, and can additionally help reduce the cost of nondiscrimination (as compared to the existing measures). We also design mechanisms to incorporate these new measures into the design of convex boundary-based classifiers.

First-order (FO) transition systems have recently attracted attention for the verification of parametric systems such as network protocols, software-defined networks or multi-agent workflows. Desirable properties of these systems such as functional correctness or non-interference have conveniently been formulated as safety properties. Here, we go one step further. Our goal is to verify safety, and also to develop techniques for automatically synthesizing strategies to enforce safety. For that reason, we generalize FO transition systems to FO safety games. We prove that the existence of a winning strategy of safety player in finite games is equivalent to second-order quantifier elimination. For monadic games, we provide a complete classification into decidable and undecidable cases. For games with non-monadic predicates, we concentrate on universal invariants only. We identify a non-trivial sub-class where safety is decidable. For the general case, we provide meaningful abstraction and refinement techniques for realizing a CEGAR based synthesis loop. Joint work with: Christian Müller, TUM Bernd Finkbeiner, Universität des Saarlandes

Many approaches to analyze the complexity of programs automatically are based on transformations into integer or term rewrite systems. However, state-of-the-art tools that analyze the worst-case complexity of rewrite systems are restricted to the inference of upper bounds. In this talk, the first techniques for the inference of lower bounds on the worst-case complexity of integer and term rewrite systems are introduced. While upper bounds can prove the absence of performance-critical bugs, lower bounds can be used to find them.

For term rewriting, the power of the presented technique gives rise to the question whether the existence of a non-constant lower bound is decidable. Thus, the corresponding decidability results are also discussed in this talk.

Finally, to see the practical value of complexity analysis techniques for rewrite systems, we will have a glimpse at the transformation from Java programs to integer rewrite systems that is implemented in the tool AProVE.