Chair of Computer Science II - Software Engineering


    TraceCORONA - Anonymous contact tracing for pandemic response

    The new strain of coronavirus causing the respiratory disease COVID-19 spreads rapidly around the globe and became a global pandemic in just 3-4 months from the beginning of its existence (reference). The social distancing and contact tracing became key strategies in slowing down the spread of the virus, and it is likely they will remain primary countermeasures until specific COVID-19 treatment becomes available.

    So far, contact tracing is often performed manually and requires significant efforts from public health stuff workers to help infected patients to recall their close social contacts and use this information to build infection chains (reference). While obtained with high efforts, this information may be incomplete or even erroneous; resulting in overestimation extra costs for unnecessary testing and underestimation and missing cases. Hence, new methods for more reliable, precise and cost-effective contact tracing are needed.

    Using advanced digital tracing apps on mobile devices can help reduce manual effort and significantly increase tracing accuracy. This has already been successfully demonstrated in Asia (e.g., Singapore, China, Korea). However, Asian tracking technologies do not consider privacy requirements and collect highly sensitive data from individuals, such us their geographical locations and encountered contacts, by centralized servers. However, different counties have different data protection and privacy regulations, and in particular, US and European countries have more restrictive regulations that prohibit collection of privacy-sensitive data in clear form. Hence, in Europe and US scientists and technologists are investing effort in developing tracing apps that can provide an appropriate level of privacy.

    Our approach, dubbed TraceCORONA, uses Bluetooth Low Energy and the ECDH key exchange algorithm  to exchange tokens, which stay on the users' local devices. The tokens are only distributed among other app users in case of confirmed infection, and even in this case the tokens are disseminated in anonymized form. Only the owners of the original token, which are the two people who originally met, can identify the contact match; figure out when the contact was established for how long it lasted. Hence, the solution is privacy preserving and rules out tracing of users or de-anonymizing infected and endangered users even by the service provider who is collecting and disseminating encounter tokens.


    TraceCORONA is developed by an international team of researchers and industry partners spearheaded by the System Security Lab at TU Darmstadt. We are proud to be part of this project.

    More information at https://tracecorona.net/

    People involved: Prof. A. Dmitrienko, Filipp Roos

    (Federated) Machine Learning for Risk Detection on Mobile Platforms

    The importance of security and protection of private information on mobile devices has increased in recent years due to the widespread use of these devices. This lead to the intensive use of mobile platforms for security-critical tasks, such as online banking, mobile payments, healthcare applications or business-related activities. On another hand, mobile platforms became attractive attack targets, since they store and process a significant amount of security-critical information, such as authentication credentials, payment information, and access tokens.

    While the protection of security-critical information in mobile apps is paramount to the security of mobile services, the often-used BYOD (Bring Your Own Device) paradigm makes the protection challenging. With BYOD, users may install arbitrary apps on their mobile devices, including malicious apps that can interfere with security-sensitive logic. On another hand, mobile service providers are limited in what defense mechanisms they can deploy for protection, since no additional requirements on underlying hardware can be assumed for interoperability reasons, and any OS-level protections are inapplicable, too, since those would void the warranty of mobile platform vendors. Hence, defenders need to resort to lightweight application-level defense strategies, such as application hardening, app-level monitoring and intrusion detection. This approach, however, typically relies on data collection and fingerprinting of platform features – an approach that is associated with privacy risks for users.

    Project Goal. In this project, the goal is to build a lightweight framework for risk detection on mobile platforms, which applies machine learning (ML) and artificial intelligence (AI) methods for risk detection, while remaining privacy-friendly towards end-users. The examples of risks the project aims to address are co-installed malicious apps, jailbreaks, code injection, clickjaking/UI-redressing attacks, and device theft, to name some. The privacy friendliness is achieved through the application of a concept of so-called Federated Machine Learning (FML), which allows for the predictive ML models training directly on the devices, thereby eliminating the need for centralized collection and processing of user data. The central aggregation service in FML is only responsible for the collection of locally trained models and their integration into a global model, but not for the collection of end-user data. Once aggregated, the global model can be re-distributed among the clients, thus improving the precision of local models through the knowledge obtained on other platforms. This approach enables devices to learn models in collaboration while keeping all training data local.


    Overall, the FML-based risk detection method provides the following advantages:

      1. Secure: Since the user data is never collected by the service provider, there is no risk of server-side security breaches
      2. Privacy-preserving: The solution is GDPR (General Data Protection Regulation)  friendly since the collected data is never sent to the service provider
      3. Precise: Privacy-preserving treatment of data enables collection of data in higher volumes, which results in larger datasets and more precise detection models
      4. Adaptive: The model continues to evolve through iterations of aggregation and re-distribution cycles


    The project is funded by KOBIL Security Systems GmbH, and is executed in cooperation with KOBIL and TU Darmstadt.

    People involved: Prof. A. Dmitrienko, Christoph Sendner, Filip Roos, Lukas Nothhelfer

    SIMPL: Secure Internet of Things Management Platform

    The SIMPL project aims to develop a framework for secure communication in heterogeneous and dynamic networks, such as the Internet of Things (IoT).

    Together with the industry partners Infosim, Mixed Mode, and Hahn-Schickard, the Secure Software Systems Group and the Software Engineering group are developing a communication middelware to facilitate secure information exchange between IoT devices. Currently, most of these devices communicate insecurely through a variety of protocols. The SIMPL framework aims to provide a secure layer for message encryption and authentication, permission and key management, as well as trust establishment. To prevent vendor lock-in and single points of failure, decentralized approaches to achieve trust consensus will be explored in this project, for example through the use of blockchains.


    The SIMPL project is funded by the Bundesministerium für Bildung und Forschung, and has a duration of three years (July 2018 to June 2021).

     Project description on the BMBF website (german)

    Project homepage