  The performance of machine learning algorithms can be considerably improved
when trained over larger datasets. In many domains, such as medicine and
finance, larger datasets can be obtained if several parties, each having access
to limited amounts of data, collaborate and share their data. However, such
data sharing introduces significant privacy challenges. While multiple recent
studies have investigated methods for private collaborative machine learning,
the fairness of such collaborative algorithms was overlooked. In this work we
suggest a feasible privacy-preserving pre-process mechanism for enhancing
fairness of collaborative machine learning algorithms. Our experimentation with
the proposed method shows that it is able to enhance fairness considerably with
only a minor compromise in accuracy.
