Open Source AI Project


Cheetah is a project aimed at enabling lean and fast secure two-party deep neural network inference.


The Cheetah project represents a significant advancement in the field of privacy-preserving artificial intelligence, specifically focusing on the domain of deep neural network inference. Its primary goal is to address the challenges associated with performing secure inference tasks, which are essential for ensuring privacy and security when AI systems process sensitive data. Secure inference involves executing neural network models in such a way that the data used for inference remains confidential, and the results are generated without exposing the underlying data to either party involved in the computation.

One of the main hurdles in deploying privacy-preserving AI solutions, especially in environments where computational resources are limited, is the high computational cost traditionally associated with secure inference methods. These methods typically require complex cryptographic operations or other privacy-enhancing technologies that can significantly slow down the processing speed and increase the computational burden.

Cheetah aims to overcome these challenges by introducing optimizations that reduce the computational overhead of secure inference. This means that it makes the process of performing secure deep neural network inference more efficient, requiring less computational power and time. As a result, it becomes more practical to implement privacy-preserving AI solutions in settings where computational resources are scarce or where fast response times are critical.

By enabling lean and fast secure two-party deep neural network inference, Cheetah facilitates the wider adoption of privacy-preserving AI technologies. This is particularly important in applications where protecting the privacy of the data being analyzed is paramount, such as in healthcare, finance, and personal assistants. With Cheetah, organizations and developers can deploy AI models that not only deliver accurate and powerful insights but also safeguard the confidentiality of the data involved, making advanced AI accessible in a wider range of contexts without compromising on security and privacy.

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