Privacy Preserving Machine Learning Techniques
Privacy Preserving Machine Learning Techniques


Privacy-preserving machine learning is an emerging field that is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralized techniques are great, there are plenty of scenarios such as user privacy, legal concerns, business competitiveness, or bandwidth limitations, wherein data cannot be aggregated together. Federated Learning can help overcome all these challenges with its decentralized strategy for building machine learning models. Paired with privacy-preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions.

In this talk, I’ll be bringing the audience up to speed with the progress in Privacy-preserving machine learning while discussing platforms for developing models and present a demo on healthcare use cases.


Amogh Kamat Tarcar is a Team Lead at Persistent Systems, exploring machine learning in CTO R&D team.

Open Data Science




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