Privacy Preserving Machine Learning
Basic Information
Content of the Course Although the applications of machine learning seem to be endless, many countries restrict and regulate the handling and usageof personal data by data protection regulations such as the EU GDPR. Many companies still struggle with the implementation and maintenance of EU GDPR conform data handling. Especially in conservative markets the usage of complex models or even the sto- rage of related data is obviated. The biggest challenge at present is to meet the requirements of the data protection regulations whi- le opening up new markets at the same time. Therefore, a variety of new technologies that enable privacy preserving machine lear- ning have emerged during the recent years. These techniques aim to protect machine learning models from a variety of attacks that try to reveal data, training features, or the algorithm itself.
The objective of this seminar is to perform an extensive analysis of the state of the art in which privacy threats and the implementa- tion of counter-measures will be analysed.
Registration:
Exam Topics are in the area of:
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Privacy preserving machine learning in the smartphone ecosystem
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Privacy preserving machine learning in the car industry
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Economic incentives for privacy preserving machine learning
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Privacy preserving federated learning
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Privacy preserving differential privacy
Specific topics will be provided during the kick-off session before the registration. The methodologies will be presented and discussed in the group before the allocation of the topics.
Students are still required to work through the methodology of their topic carefully.
