Fairness-Driven Private Collaborative Machine Learning

Dana Pessach, Tamir Tassa, Erez Shmueli

Research output: Contribution to journalArticlepeer-review


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 has been overlooked. In this work, we suggest a feasible privacy-preserving pre-process mechanism for enhancing fairness of collaborative machine learning algorithms. An extensive evaluation of the proposed method shows that it is able to enhance fairness considerably with only a minor compromise in accuracy.

Original languageEnglish
Article number27
Pages (from-to)1-30
Number of pages30
JournalACM Transactions on Intelligent Systems and Technology
Issue number2
StatePublished - 22 Feb 2024

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  • algorithmic fairness
  • collaborative machine learning
  • federated learning
  • Privacy
  • secure multi-party computation


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