Authors:
Tobias Müller
1
;
2
;
Nadine Gärtner
2
;
Nemrude Verzano
2
and
Florian Matthes
1
Affiliations:
1
Chair for Software Engineering for Business Information Systems, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching bei München, Germany
;
2
SAP SE, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany
Keyword(s):
Big Data, Anonymization, Encryption, Data Markets, Privacy-enhancing Techniques, Federated Learning.
Abstract:
Research in federated machine learning and privacy-enhancing technologies has spiked recently. These technologies could enable cross-company collaboration, which yields the potential of overcoming the persistent bottleneck of insufficient training data. Despite vast research efforts and potentially large benefits, these technologies are only applied rarely in practice and for specific use cases within a single company. Among other things, this little and specific utilization can be attributed to a small amount of libraries for a rich variety of privacy-enhancing methods, cumbersome design of end-to-end privacy-enhancing pipelines and unwieldy cus- tomizability to needed requirements. Hence, we identify the need for an easy-to-use privacy-enhancing tool to support and enable cross-company machine learning, suitable for varying scenarios and easily adjustable to the desired corresponding privacy-utility desiderata. This position paper presents the starting point for our future work aim
ing at the development of the described application.
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