Original Event: https://www.meetup.com/google-developer-group-san-francisco/events/254262715/ Agenda : Talk: Implement End-to-End Encryption & HIPAA with Firebase Talk: End to End Machine Learning About the Speakers: David Szabo is a Privacy activist & crypto(graphy) nerd: I'm working on making and end to data breaches. In my third startup, Virgil Security, I'm building a framework for developers to simplify the implementation of perfect user privacy in healthcare, IoT and other products. Earlier in my career, I spent 12 years at Microsoft across 4 countries in various engineering, UX, business and leadership roles. Vikram Tiwari is the co-founder of Omni Labs based in San Francisco which aims to bring data analytics and machine learning in marketing space. He is also a GDE for Google Cloud and Machine Learning. He focuses on big data and machine learning topics. Other than work he is involved with various communities in open source and local tech meetups. • Important to know
6:00pm - 6:30pm Check-in and networking over food and drinks
6:30pm: Talks
Implement End-to-End Encryption & HIPAA with Firebase
End to End Machine Learning
Description: Many developers see Firebase as a prototyping platform, which the platform way outgrew by now with a total of 18 products covering pretty much everything a large production app needs. In this talk, David Szabo will introduce about an open source project to implement end-to-end encryption on top of Firebase/Firestore, making Firebase one of the most secure cloud platforms out there. End-to-End Encryption also enables healthcare apps to run encrypted chats on the platform that meet HIPAA's requirements. The talk will cover the basic principle of how an end-to-end encrypted key management works, how keys can be distributed securely between users with open source libraries and how end-users can own the keys without noticing any extra overhead. A sample iOS and Android app/source will be used to demonstrate parts of the solution and outline how HIPAA deals with data encryption.
Description: We will start by developing a ML model using TensorFlow, training and deploying it on Google Cloud and then finally use it to get inferences.
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