Privacy Preserving Machine Learning Techniques
Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised 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 Learningcan help overcome all these challenges with its decentralised 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 upto speed with the progress in Privacy preserving machine learning while discussing platforms for developing models and present a demo on healthcare use cases.
Outline/Structure of the Demonstration
In this talk I’ll be introducing the audience to the emerging field of Privacy preserving machine learning which sits right at the intersection of decentralised machine learning and privacy preserving techniques. The talk will include overview of current platforms along with use case demonstration.
Timewise outline :
00:00- 01:00 : Intro
01:00 - 03:00 :Need for Privacy Aware Machine Learning
03:00 -07:00 : Federated Learning Intro+FL in healthcare
07:00-12:00: Privacy concerns + Tools & Platforms
12:00 -18:00 :Demo
This novel technique presents new opportunities for businesses to work around private and sensitive datasets. This demonstration will help audience understand building blocks of developing privacy preserving machine learning.
Machine Learning Engineers, Data Scientists, Product Managers
Prerequisites for Attendees
Knowledge of current machine learning techniques.
Hi, I am working as a machine learning researcher with the R&D team at Persistent Systems. In my role I have actively kept tabs on latest machine learning trends and hands-on with bleeding edge implementations. I have experience in building NLP solutions as well as on-device ML implementations. Presently i am researching on privacy preserving machine learning and have recently completed Secure and Private AI course https://www.udacity.com/course/secure-and-private-ai–ud185 . Linkedin : https://www.linkedin.com/in/amogh-kamat-tarcar-a6367b1b Twitter Handle: @aktarcar I have also created twitter list for federated learning : https://twitter.com/i/lists/1202453611034210304?s=20 .
schedule Submitted 3 years ago
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If there is anything that blocks you today from trying new techniques, or keeps you wondering how and where to start from, or anything that I could help you with, please leave a comment and I will work to get answers to, in this talk (if the talk gets accepted, if not pls reach out to me on linkedIn and I will be happy to help.).