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Franziska Boenisch, M.Sc.

SSE - Fraunhofer AISEC

Institut für Informatik

Fachbereich Mathematik und Informatik

Wissenschaftliche Mitarbeiterin

Differential Privacy, Private and Secure Machine Learning

Breite Str. 12
Raum 135
14199 Berlin
  • Since 09/2019: Research Assistant at department Secure Systems Engineering (SSE), Fraunhofer AISEC
  • 2019: M.Sc. in Computer Science at Freie Universität Berlin and Technical University Eindhoven
  • 2017: B.Sc. in Computer Science at Freie Universität Berlin


Franziska Boenisch, Adam Dziedzic, Roei Schuster, Ali Shahin Shamsabadi, Ilia Shumailov and Nicolas Papernot, 2021:
"When the Curious Abandon Honesty: Federated Learning Is Not Private."
arXiv preprint arXiv:2112.02918. 

Franziska Boenisch, 2021:
"A Systematic Review on Model Watermarking for Neural Networks."
Frontiers in Big Data, 4(96). 

Franziska Boenisch, Reinhard Munz, Marcel Tiepelt, Simon Hanisch, Christiane Kuhn, and Paul Francis, 2021:
"Side-Channel Attacks on Query-Based Data Anonymization."
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS’21), November15–19,2021,Virtual Event, Republic of Korea. 

Franziska Boenisch Verena Battis, Nicolas Buchmann, and Maija Poikela, 2021:
"“I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners."
Mensch und Computer 2021, 520-546.

Sörries, Peter, Claudia Müller-Birn, Katrin Glinka, Franziska Boenisch, Marian Margraf, Sabine Sayegh-Jodehl, and Matthias Rose, 2021:
"Privacy Needs Reflection: Conceptional Design Rationales for Privacy-Preserving Explanation User Interfaces."
Mensch und Computer 2021, Workshow-Proceedings.

Franziska Boenisch, 2021:
"Privatsphäre und Maschinelles Lernen."
Datenschutz Datensicherheit 45, 448–452.

Franziska Boenisch, Philip Sperl, and Konstantin Böttinger, 2021:
"Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning."
arXiv preprint arXiv:2105.07985

Boenisch, Franziska, 2019:
Applying Differential Privacy to Machine Learning: Challenges and Potentials
31. Krypto-Tag, Gesellschaft für Informatik Fachgruppe Angewandte Kryptographie, Berlin, 2019 (Proceedings).

Boenisch, Franziska, 2019:
"Differential Privacy: General Survey and Analysis of Practicability in the Context of Machine Learning"
Freie Universität Berlin, 2019. (Thesis).

Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David Dormagen, Fernando Wario, and Tim Landgraf, 2018:
"Tracking all members of a honey bee colony over their lifetime using learned models of correspondence."
Frontiers in Robotics and AI. 5(35).

Boenisch, Franziska, 2017:
"Feature Engineering and Probabilistic Tracking on Honey Bee Trajectories"
Freie Universität Berlin, 2017. (Thesis)


  • Winter 19/20: Security Protocols and Infrastructure (Tutorial).