E5 - Privacy-Aware Distributed Computation

E5 – Privacy-Aware Distributed Computation


The project addresses the challenge of automated generation of scalable privacy-preserving mechanisms in IoT, cloud, and edge computing systems. The project designs SecQL, a query language for data-intensive applications, whose runtime environment automatically generates and deploys sub-computations over nodes of the above systems to optimize performance while protecting the processed data from unauthorized access. The formally defined SecQL makes privacy-preserving mechanisms accessible also for non-security experts.


Mirko Köhler
Reactive Software Systems

Research Interests:

  • Programming languages for distributed systems

Aditya Oak
Software Technology Group

Research Interests:

  • Programming languages for distributed systems


Schulz, Philipp (2019):
Developing Secure Distributed Systems with Modular Tierless Programming.
TU Darmstadt, [Masterarbeit]

Srivatsa, Jeevan Karanam (2019):
Benchmarking of Data Provenance Computation Methods to Support Debugging in Apache Spark.
TU Darmstadt, [Masterarbeit]

Oak, Aditya ; Mezini, Mira ; Salvaneschi, Guido (2019):
Language Support for Multiple Privacy Enhancing Technologies.
ACM, In: Conference Companion of the 3rd International Conference on Art, Science, and Engineering of Programming, [Konferenzveröffentlichung]

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