Hardware Industry

Hardware Data:

Vision data

Machine generated data

Allocations data

1. Gain insights on machine operation

Gaining insights on the performance of machines across factories and businesses is crucial to validate their continued reliable operation. However, in many cases, the end employer of the machine will be reluctant to share such information. Allowing anomaly detection and generation of insights from combined machine data without exposing information of a single machine (which is considered the individual whose privacy we want to preserve in this scenario) can generate valuable benefits.

Learn how we enable privacy-preserving insights

2. Protect AI chips against inversion attacks

Supervised machine learning models trained on sensitive data can leak information about the training dataset. This means the model owner should protect its intellectual property: the trained model and the labeled dataset (which is usually resource-intensive to obtain).

Over the past decade, researchers have repeatedly demonstrated how model inversion attacks can threaten privacy with remarkable ease. Therefore, hardware security techniques should be utilized to protect companies' valuable IP and guarantee privacy of the end models. This can be done by generating models using privacy-preserving training methods that are reverse-engineering-proof.

Read more about model inversion on our blog

Want to know if your use-case can benefit from PVML's platform? Contact us!