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Franziska Boenisch, M.Sc.

FranziskaBoenisch

SSE - Fraunhofer AISEC

Institut für Informatik

Fachbereich Mathematik und Informatik

Wissenschaftliche Mitarbeiterin

Differential Privacy, Private and Secure Machine Learning

Adresse
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

Publications

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. 
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Franziska Boenisch, 2021:
"A Systematic Review on Model Watermarking for Neural Networks."
Frontiers in Big Data, 4(96). 
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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. 
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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.
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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.
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Franziska Boenisch, 2021:
"Privatsphäre und Maschinelles Lernen."
Datenschutz Datensicherheit 45, 448–452.
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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
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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).
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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).
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Boenisch, Franziska, 2017:
"Feature Engineering and Probabilistic Tracking on Honey Bee Trajectories"
Freie Universität Berlin, 2017. (Thesis)
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Teaching

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