Products.

TensorFlow Privacy

Georgian Partners has made its differentially private machine learning software available as part of TensorFlow Privacy. This software 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 TensorFlow Privacy, you can guarantee your customers' privacy, earn their trust, gain access to more data, and ultimately improve your products.

With our addition TensorFlow Privacy now supports two Logistic Regression and Support Vector Machines in addition to deep learning to help bring privacy guarantees to your AI solutions.

Frequently Asked Questions.

“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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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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