The Conference for Machine Learning Innovation

Privacy-preserving Machine Learning

Session
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
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Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Infos
Tuesday, December 10 2019
17:00 - 17:45
Room:
Salon 6+7

Privacy-preserving machine learning is a subfield of machine learning in which the training of the model happens in such a way that the privacy of the data is preserved. Various approaches already exist but are not well established. At the same time, privacy considerations become more important. Among the approaches is federated learning for a decentralized training, whereby the data can stay at the place of origin and only learning updates or gradient updates are exchanged. Another approach is differential privacy – stochastic gradient descent whereby the learning algorithm of the neural network is modified so that single training examples do not affect the model too much. Thus, limited inference can be made from the model to the data it was trained on. In this talk we will understand both approaches and have a look on how to implement them with the help of TensorFlow.

Behind the Tracks