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Matthias Matousek

Bis zum Sommer 2015 studierte ich Medieninformatik an der Universität Ulm. Ich verbrachte außerdem von 2013 bis 2014 ein Jahr in den Niederlanden. Dort besuchte ich an den Hochschulen Universität Twente, Technische Universität Eindhoven und Radboud Universität Nijmegen Lehrveranstaltungen des Kerckhoffs Institute zum Thema Computer Security. In meiner Masterarbeit beschäftigte ich mich mit einem sicheren Prozessor mit dem Titel Secure Execution PUF-based Processor (SEPP). Seit dem Sommer 2015 arbeite ich als Wissenschaftlicher Mitarbeiter beim Institut für Verteilte Systeme.

Forschung

Ich interessiere mich generell für Security und Privacy in der Informatik. Speziell arbeite ich an der Privacy im Kontext von vernetzten Fahrzeugen. Zusätzlich interessiere ich mich für private und sichere Kommunikation (wie beispielsweise den Einsatz von Kryptographie in Messenger Diensten), sowie diese benutzerfreundlich zu realisieren.

Lehre

Projekt

Seminare

Abschlussarbeiten und studentische Projekte

Gerne betreue ich studentische Abschlussarbeiten aus dem Themenfeld meiner Forschungsinteressen. Im Folgenden sehen Sie aktuell von mir angebotene Themen für Abschlussarbeiten und Projekte. Laufende und abgeschlossene Arbeiten können hier eingesehen werden.

Verfügbare Themen


Matousek, Matthias
Security Analysis of Home Assistant
Master's thesis, Bachelor's thesis, Project
Institute of Distributed Systems,
2019
in Vorbereitung

Zusammenfassung: Home automation is becoming more and more popular. Many companies sell sensors and actuators to automate lights, doors, vacuum robots, plant watering, etc. While many products rely on closed-source control software, which often lives in the cloud, there are also open-source alternatives. Open-source projects like Home Assistant aim to provide integration for home automation solutions of many different vendors and also to give full control to the user without compromising privacy or becoming dependant on specific operators. A software that has so much data about peoples' personal lives should fulfill high security requirements. The goal of this thesis or project is to conduct a methodical security analysis of Home Assistant and to document the outcomes.

Matousek, Matthias
Machine Learning on Encrypted Data
Master's thesis, Bachelor's thesis, Project
Institute of Distributed Systems,
2019
in Vorbereitung

Zusammenfassung: Machine Learning enables great applications, such as voice assistants and image recognition. However, in most cases, it is required to send the input data to another party with powerful machine learning models and lots of computing power, in order to utilize the power of machine learning. This is a risk for privacy. Libraries like tf-encrypted and PySyft aim to address this issue by implementing encryption mechanisms that allow machine learning on encrypted data. The goal of this thesis or project is to understand how encrypted machine learning techniques work and how they get implemented with tf-encrypted and/or PySyft. Further, it is possible to extend on this by comparing different libraries and techniques or by implementing own encrypted machine learning techniques.

Matousek, Matthias
Privacy-Preserving First Responder Alert System
Master's thesis, Bachelor's thesis, Project
Institute of Distributed Systems,
2019
in Vorbereitung

Zusammenfassung: In a medical emergency every minute counts. While emergency services are generally very quick, first responders can have immense positive impact on a patient's further recovery and sometimes even their chance of survival. Trained first responders are often already part of many companies and most people have some first aid training that enables them to help in medical emergencies. However, what if an emergency happens quite close to a well-trained first responder (or even a medical professional), but this person just does not know about it? With today's prevalent smartphone and wearable technology, it is obvious to integrate it in rescue operations. A service can automatically track the locations of first responders and dispatch them accordingly. Such a system can help save lives, but it comes with a big privacy issues: the first responders must be location-tracked. This could be a reason for someone not to register with such a service. Furthermore, large scale tracking may be problematic from a legal perspective as well (consider the EU's General Data Protection Regulation). The goal of this project or thesis is to develop a prototype of a privacy-preserving first responder alert system by devising an architecture, analyzing privacy issues, and finally selecting and implementing suitable privacy-enhancing technologies.

Matousek, Matthias
Machine Learning with TensorFlow Federated
Master's thesis, Bachelor's thesis, Project
Institute of Distributed Systems,
2019
in Vorbereitung

Zusammenfassung: To build powerful machine learning models, lots of data is required. However, obtaining the data comes with privacy risks for the people or entities that provide their data. Recently, Google published TensorFlow Federated - an open source framework to allow machine learning on decentralized data. The approach of federated learning makes machine learning in the age of mobile devices and wearables both more efficient, as well as more privacy-friendly. The goal of this thesis or project is to become familiar with the TensorFlow Federated framework, to understand and be able to explain the techniques which are implemented in it, to be able to build machine learning models in a federated way, and possibly to implement own enhancements of the framework.

Matousek, Matthias
Machine Learning with TensorFlow Privacy
Master's thesis, Bachelor's thesis, Project
Institute of Distributed Systems,
2019
in Vorbereitung

Zusammenfassung: Machine learning offers great opportunities, but also comes with risks. Especially the privacy risks are becoming more prevalent in the discussions about machine learning. Recently, Google published a machine learning library called TensorFlow Privacy. Its goal is to make it easier for developers and researchers to build privacy-preserving machine learning models. Specifically, it utilizes Differential Privacy, which mathematically guarantees that the training data to create the models is protected from being extracted. The goal of this thesis or project is to become familiar with the TensorFlow Privacy library, to understand and be able to explain the techniques which are implemented in it, to be able to build privacy-preserved machine learning models, and possibly to implement own protection techniques that could enhance the TensorFlow Privacy library.

Publikationen


2018

Kleber, Stephan; Unterstein, Florian; Hiller, Matthias; Slomka, Frank; Matousek, Matthias; Kargl, Frank; Bösch, Christoph
Secure Code Execution: A Generic PUF-driven System Architecture
21st Information Security Conference
Oktober 2018
akzeptiert
Matousek, Matthias; Bösch, Christoph; Kargl, Frank
Poster: Privacy-Preserving Decision Trees
Privacy in Machine Learning and Artificial Intelligence Workshop at ICML 2018
Juli 2018
akzeptiert
Matousek, Matthias; Yassin, Mahmoud; Al-Momani, Ala'a; van der Heijden, Rens W.; Kargl, Frank
Robust Detection of Anomalous Driving Behavior
IEEE 87th Vehicular Technology Conference (VTC)
Juni 2018

2016

Berlin, Olga; Held, Albert; Matousek, Matthias; Kargl, Frank
POSTER: Anomaly-Based Misbehaviour Detection in Connected Car Backends
2016 IEEE Vehicular Networking Conference (VNC)
Oktober 2016
Matousek, Matthias; Bösch, Christoph; Kargl, Frank
Using Searchable Encryption to Protect Privacy in Connected Cars
Proceedings of the 4th GI/ITG KuVS Fachgespräch Inter-Vehicle Communication
2016

2015

Kleber, Stephan; Unterstein, Florian; Matousek, Matthias; Kargl, Frank; Slomka, Frank; Hiller, Matthias
Design of the Secure Execution PUF-based Processor (SEPP)
Workshop on Trustworthy Manufacturing and Utilization of Secure Devices, TRUDEVICE 2015
September 2015
Kleber, Stephan; Unterstein, Florian; Matousek, Matthias; Kargl, Frank; Slomka, Frank; Hiller, Matthias
Secure Execution Architecture based on PUF-driven Instruction Level Code Encryption
IACR,
Juli 2015

2012

Nikolov, Vladimir; Matousek, Matthias; Rautenbach, Dieter; Draque Penso, Lucia; Hauck, Franz J.
ARTOS: System Model and Optimization Algorithm
Dokument Nummer: VS-R08-2012
Institute of Distributed Systems, University of Ulm,
Dezember 2012
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