Products.

Epsilon v1.0

Georgian Partners Epsilon v1.0 is our differentially private machine learning software. 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.

“The Impact team made a tremendous contribution to our business. Thanks to their input, we now see differential privacy as one of our main points of differentiation.”

Mahmoud Arram, Co-founder & CTO, Bluecore

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Frequently Asked Questions.

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Introducing Georgian Partners Epsilon v1.0

By Madalin Mihailescu

We’re excited to announce the release of Georgian Partners Epsilon v1.0, our machine learning (ML) product that helps bring privacy guarantees to our portfolio companies’ AI solutions. We believe that…

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What Is Differential Privacy?

By Madalin Mihailescu

In a world where the risks and costs associated with privacy are on the rise, differential privacy offers a solution. Simply put, differential privacy is a mathematical definition of the…

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What is Differential Privacy and Why Does it Matter?

By Georgian Partners

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…

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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.

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Case studies.

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.

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