Matthias Huber

Mining edge-weighted call graphs to localise software bugs

An important problem in software engineering is the automated discovery of noncrashing occasional bugs. In this work we address this problem and show that mining of weighted call graphs of program executions is a promising technique. We mine weighted graphs with a combination of structural and numerical techniques. More specifically, we propose a novel reduction technique for call graphs which introduces edge weights. Then we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection schemes. The technique generalises previous graph mining approaches as it allows for an analysis of weights. Our evaluation shows that our approach finds bugs which previous approaches cannot detect so far. Our technique also doubles the precision of finding bugs which existing techniques can already localise in principle.

Cumulus4j: A Provably Secure Database Abstraction Layer

Cloud Computing has huge impact on IT systems. It offers advantages like flexibility and reduced costs. Privacy and security issues, however, remain a major drawback. While data can be secured against external threats using standard techniques, service providers themselves have to be trusted to ensure privacy.

On the Usefulness of Weight-Based Constraints in Frequent Subgraph Mining ?

Frequent subgraph mining is an important data-mining technique. In this paper we look at weighted graphs, which are ubiquitous in the real world. The analysis of weights in combination with mining for substructures might yield more precise results. In particular, we study frequent subgraph mining in the presence of weight-based constraints and explain how to integrate them into mining algorithms. While such constraints only yield approximate mining results in most cases, we demonstrate that such results are useful nevertheless and explain this effect. To do so, we both assess the completeness of the approximate result sets, and we carry out application-oriented studies with real-world data-analysis problems: software-defect localization and explorative mining in transportation logistics. Our results are that the runtime can improve by a factor of up to 3.5 in defect localization and 7 in explorative mining. At the same time, we obtain an even slightly increased defect-localization precision and obtain good explorative mining results.

MimoSecco: A Middleware for Secure Cloud Storage

The biggest impediment for the adoption of cloud computing practices is the lack of trust in the confidentiality of one’s data in the cloud. The prevalent threat in the cloud computing model are so-called insider attacks. Full data encryption can only solve the problem in the trivial case of backups. Any sophisticated service provided on data requires insight into the structure of that data. One purpose of encryption is to prevent such insights. We introduce the MimoSecco project. In MimoSecco, we are investigating reasonable compromises. We employ two techniques, separation of duties and secure hardware. With separation of duties, we fragment a database and separate the fragments geographically. The goal is to make it infeasible to reconstruct the database from one fragment alone. The secure hardware tokens we employ are hard-to-copy devices which offer encryption, decryption and cryptographically signing of data. The keys used are stored in the tamper-proof hardware device and never leave it. We are in the process of developing a prototypical database adapter that behaves like a SQL database, but stores data securely.

Privacy-aware folksonomies

Many popular web sites use folksonomies to let people label objects like images (Flickr), music (Last.fm), or URLs (Delicous) with schema-free tags. Folksonomies may reveal personal information. For example, tags can contain sensitive information, the set of tagged objects might disclose interests, etc. While many users call for sophisticated privacy mechanisms, current folksonomy systems provide coarse mechanisms at most, and the system provider has access to all information. This paper proposes a privacy-aware folksonomy system. Our approach consists of a partitioning scheme that distributes the folksonomy data among four providers and makes use of encryption. A key sharing mechanism allows a user to control which party is able to access which data item she has generated. We prove that our approach generates folksonomy databases that are indistinguishable from databases consisting of random tuples.

Brief Announcement: Towards Secure Cloud Computing

Security of cloud computing in the strict cryptographic sense is impossible to achieve practically. We propose a pragmatic security approach providing application-specific security under practical constraints.

IMHOTEP: virtual reality framework for surgical applications

PurposeThe data which is available to surgeons before, during and after surgery is steadily increasing in quantity as well as diversity. When planning a patient’s treatment, this large amount of information can be difficult to interpret. To aid in processing the information, new methods need to be found to present multimodal patient data, ideally combining textual, imagery, temporal and 3D data in a holistic and context-aware system.MethodsWe present an open-source framework which allows handling of patient data in a virtual reality (VR) environment. By using VR technology, the workspace available to the surgeon is maximized and 3D patient data is rendered in stereo, which increases depth perception. The framework organizes the data into workspaces and contains tools which allow users to control, manipulate and enhance the data. Due to the framework’s modular design, it can easily be adapted and extended for various clinical applications.ResultsThe framework was evaluated by clinical personnel (77 participants). The majority of the group stated that a complex surgical situation is easier to comprehend by using the framework, and that it is very well suited for education. Furthermore, the application to various clinical scenarios—including the simulation of excitation propagation in the human atrium—demonstrated the framework’s adaptability. As a feasibility study, the framework was used during the planning phase of the surgical removal of a large central carcinoma from a patient’s liver.ConclusionThe clinical evaluation showed a large potential and high acceptance for the VR environment in a medical context. The various applications confirmed that the framework is easily extended and can be used in real-time simulation as well as for the manipulation of complex anatomical structures.