Join for free and connect with our local tech scene

Stay on top of the latest companies and upcoming events with our weekly newsletter, and be counted among the people building the future of your local tech community.

Cleveland, OH /

Advancements in Deep Learning Privacy and Encryption with Jason Mancuso

Basement Theatre of Crown Centre 1 Building 5005 Rockside Road , Independence, OH 44131 (map)

In the future, our most impactful applications will rely on our most sensitive data such as medical records, financial statements, location history, and voice transcripts. If this data were to end up in the wrong hands it could be disastrous to the individual, erode trust in the company, and lead to legal implications with the rise of policies such as GDPR. However, with recent advances in computing power and cryptographic techniques data privacy and machine learning no longer need to be adversaries.

In this talk, we introduce the importance of data privacy for advancing machine learning applications across healthcare, finance, and transportation. We discuss the many different technologies enabling this future such as differential privacy, secure multi-party computation, garbled circuits, and how they can be used to train and deploy secure, privacy-preserving machine learning models. Finally, we demonstrate how you can use these technologies today using tf-encrypted (https://github.com/mortendahl/tf-encrypted), an open source library built on-top of TensorFlow for secure, privacy-preserving machine learning.

Jason is a research scientist at Dropout Labs, the founder of the Cleveland AI Group, and a member of the AI Village at DEFCON and OpenMined communities. He works on novel methods making machine learning more performant for privacy-preserving techniques like secure computation and differential privacy, most notably by contributing to the tf-encrypted project. He has worked on a variety of safety and security problems, including safe reinforcement learning, secure and verifiable agent auditing, and adversarial machine learning. His contributions to the OpenMined project formed the foundation of the current version of PySyft, a generic platform for privacy-preserving machine learning. His previous work with the Cleveland Clinic established a state-of-the-art in blood test classification and demonstrated that machine learning can virtually eliminate the problem of medical malpractice due to contaminated blood samples. He graduated from John Carroll University with a B.Sc. in Mathematics.

Comments

Sign in to comment.