Georgian Partners Epsilon v1.0 is our differentially private machine learning product. Epsilon enables companies to quickly adopt differential privacy to provide your customers with privacy guarantees. Specifically, differential privacy measures how effective particular privacy techniques — such as inserting random noise into a dataset — are at protecting the privacy of individual data records within that dataset. With Epsilon, you can guarantee your customers' privacy, earn their trust, gain access to more data, and ultimately improve your products.
In this initial release, we’re supporting two common machine learning techniques — Logistic Regression and Support Vector Machines — to help bring privacy guarantees to your AI solutions. Epsilon v1.0 is currently in closed beta.
Frequently Asked Questions.
At a time when the risks and costs associated with privacy are on the rise, differential privacy offers a solution. Differential privacy is mathematical definition for the privacy loss that…Read More
CEO’s Guide to Differential Privacy
Differential privacy is mathematical definition for the privacy loss that results to individuals when their private information is used to create an AI product. It can be used to build customer trust, making those customers more likely to share their data with you.
Differential Privacy in Action
Hear from Bluecore Co-founder and CTO, Mahmoud Arram, about how his company partnered with the Georgian Impact team to significantly augment the performance of its machine learning models, while preserving the privacy of end consumers and the trade secrets of its clients.