Privacy preserving machine_learning_current_landscape

Mar 28, 2020Download as PPTX, PDF0 likes170 views

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.

Privacy Preserving Machine
Learning Techniques
Amogh Tarcar
Machine Learning Researcher

Index
• Need for Privacy Aware Machine Learning
• Federated Learning Intro
• FL in healthcare
• Privacy concerns
• Tools & Platforms
• Demo

Need for Privacy Aware Machine Learning
• Data sources such as EHR , X-Rays, Genomics data are inherently
sensitive and private, and there are ethical as well as legal limitations
for aggregating sensitive data
• Healthcare datasets , especially for rare diseases needs collaboration
of health providers across the world
• Privacy aware machine learning technique enables building models
without needing data to move from its source location. One such
technique is called Federated Learning.

Federated
Learning
Source : https://blog.fastforwardlabs.com/images/editor_uploads/2018-10-31-181344-federated_learning_animated_labeled.gif

Federated
Machine
Learning for
Healthcare
Source: https://blogs.nvidia.com/wp-content/uploads/2019/10/federated_learning_animation_still_white.png

Privacy Concerns
• Federated learning is a variant of
decentralized machine learning
• Even though data does not leave the
source location, the model parameters
may leak info about the data
• Federated learning needs to be
implemented using secure techniques
such as :
• Differential Privacy / Secure
Aggregation
• Secure Multi Party Computation /
Encryption
Image Source : https://www.google.com/url?sa=i&url=http%3A%2F%2Fblog.fastforwardlabs.com%2F2018%2F11%2F14%2Ffederated-
learning.html&psig=AOvVaw0tpZYKhKDMX3h5MOCEXkNC&ust=1585489636330000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCLiGuqmnvegCFQAAAAAdAAAAABAD

Tools & Platforms
TensorFlow FederatedPySyft
NVIDIA CLARA
FedAI
OWKIN Platform

The Lentani Platform provides a mobile and web-based solution for fast ad-hoc data collection in multiple languages. It uses sophisticated decision trees and integrates with web services and flat files. The platform can be used for medical device complaint collection, clinical study data collection, physician referrals, patient check-ins, and symptom collection in healthcare. Short demo examples are provided for chest discomfort and headache diagnosis clinics as well as a medical device data collection demo.

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Privacy preserving machine_learning_current_landscape

1. Privacy Preserving Machine Learning Techniques Amogh Tarcar Machine Learning Researcher

2. Index • Need for Privacy Aware Machine Learning • Federated Learning Intro • FL in healthcare • Privacy concerns • Tools & Platforms • Demo

3. Need for Privacy Aware Machine Learning • Data sources such as EHR , X-Rays, Genomics data are inherently sensitive and private, and there are ethical as well as legal limitations for aggregating sensitive data • Healthcare datasets , especially for rare diseases needs collaboration of health providers across the world • Privacy aware machine learning technique enables building models without needing data to move from its source location. One such technique is called Federated Learning.

4. Federated Learning Source : https://blog.fastforwardlabs.com/images/editor_uploads/2018-10-31-181344-federated_learning_animated_labeled.gif

5. Federated Machine Learning for Healthcare Source: https://blogs.nvidia.com/wp-content/uploads/2019/10/federated_learning_animation_still_white.png

6. Privacy Concerns • Federated learning is a variant of decentralized machine learning • Even though data does not leave the source location, the model parameters may leak info about the data • Federated learning needs to be implemented using secure techniques such as : • Differential Privacy / Secure Aggregation • Secure Multi Party Computation / Encryption Image Source : https://www.google.com/url?sa=i&url=http%3A%2F%2Fblog.fastforwardlabs.com%2F2018%2F11%2F14%2Ffederated- learning.html&psig=AOvVaw0tpZYKhKDMX3h5MOCEXkNC&ust=1585489636330000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCLiGuqmnvegCFQAAAAAdAAAAABAD

7. Tools & Platforms TensorFlow FederatedPySyft NVIDIA CLARA FedAI OWKIN Platform

8. Healthcare Use case Demo

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