FEIT Research Project Database

Machine learning privacy

Project Leader: Farhad Farokhi
Collaborators: Dali Kaafar (Macquarie University and Data61)
Sponsors: The University of Melbourne
Primary Contact: Farhad Farokhi (farhad.farokhi@unimelb.edu.au)
Keywords: cybersecurity; data mining; data privacy; information theory; machine learning
Disciplines: Electrical & Electronic Engineering

Data analysis methods using machine learning (ML) can unlock valuable insights for improving revenue or quality-of-service from, potentially proprietary, private datasets. Having large high-quality datasets improves the quality of the trained ML models in terms of the accuracy of predictions on new, potentially untested data. The subsequent improvements in quality can motivate multiple data owners to share and merge their datasets in order to create larger training datasets. For instance, financial institutes may wish to merge their transaction or lending datasets to improve the quality of trained ML models for fraud detection or computing interest rates. However, government regulations (eg, the roll-out of the General Data Protection Regulation in EU, the California Consumer Privacy Act or the development of the Data Sharing and Release Bill in Australia) increasingly prohibit sharing customer’s data without consent. This motivates the need to conciliate the tension between quality improvement of trained ML models and the privacy concerns for data sharing. Therefore, this is a need for privacy-preserving machine learning.

Further information: http://farokhi.xyz/2020/02/24/machine-learning-privacy/