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Portfolio item number 2

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Feature engineering and probabilistic tracking on honey bee trajectories

Published in Bachelor Thesis, Freie Universität Berlin, 2017

Feature engineering and model tuning to perform visual tracking of marked honey bees on a hive.

Recommended citation: Boenisch, Franziska. (2017). "Feature engineering and probabilistic tracking on honey bee trajectories." Bachelor Thesis. Freie Universität Berlin. https://www.mi.fu-berlin.de/inf/groups/ag-ki/Theses/Completed-theses/Bachelor-theses/2017/Boenisch/Bachelor-Boenisch.pdf

Tracking all members of a honey bee colony over their lifetime using learned models of correspondence

Published in Frontiers in Robotics and AI, 2018

Probabilistic object tracking framework to perform large-scale tracking of several thousand honey bees

Recommended citation: Boenisch, Franziska, et al. (2018). "Tracking all members of a honey bee colony over their lifetime using learned models of correspondence." Frontiers in Robotics and AI. 5(35). https://www.frontiersin.org/articles/10.3389/frobt.2018.00035/full

Differential Privacy: General Survey and Analysis of Practicabilityin the Context of Machine Learning

Published in Master Thesis, Freie Universität Berlin, 2019

Introduction and literature review on Differential Privacy. Implementation and performance evaluation several Differentially Privacy linear regression models.

Recommended citation: Boenisch, Franziska. (2019). "Differential Privacy: General Survey and Analysis of Practicabilityin the Context of Machine Learning." Master Thesis. Freie Universität Berlin. https://www.mi.fu-berlin.de/inf/groups/ag-idm/theseses/2019_Boenisch_MSc.pdf

Security Protocols and Infrastructures

Lecture, Freie Universität Berlin, Department of Computer Science, 2019

Worked as teaching assistant for the Master level course Security Protocols and Infrastructures. The course treated security protocols (e.g. TLS, PACE, EAC), ASN.1, certificates and related norms such as X.509/RFC5280, and public key infrastructures (PKI).

Machine Learning and IT Security

Seminary, Freie Universität Berlin, Department of Computer Science, 2020

Held a Master level seminary about Machine Learning and IT Security. The seminary covered topics about securing digital infrastructure through ML assistance, as well as protecting ML models against security and privacy violations.

Hello (brand new data) world

Seminary, Universität Bayreuth, Department of Philosophy, 2020

Held a Bachelor level invited seminary about the ethical implications of ML on society. The seminary consisted of a technical / computer science as well as a philosophical part. In the technical part, theoretical background as well as implementation details of ML algorithms were presented. The philosophy part treated subjects as the Turing test, the Chinese room argument, and discussions about dataism, surveillance, autonomous driving and autonomous weapon systems.

Privacy-Preserving Machine Learning

Software Project, Freie Universität Berlin, Department of Computer Science, 2021

I organized and held the software project “Privacy-Preserving Machine Learning” with final year Bachelor and Master students from Freie University Berlin. The goal of the project was to build a software library that allows non-privacy-expert machine learning (ML) practitioners to evaluate the privacy of their neural networks. Additionally, the tool should help non-ML-experts who are in charge with system security to get an impression about the model privacy. To evaluate the privacy, several attacks against ML models were implemented. The outcome of the software project can be found in our GitHub Repository. All project management was done with Scrum where I acted as a Product Owner and the students as the Developer Team.

Trustworthy Machine Learning

Seminary, Freie Universität Berlin, Department of Computer Science, 2022

Held a Master level seminary about Trustworthy Machine Learning. The seminary covered the following topics:

• integrity attacks against ML models at training time
• integrity attacks and defenses against ML models at test time
• ML model confidentiality
• privacy attacks against ML models
• differential privacy
• fairness and ethics
• trustworthiness in federated learning