Securing Healthcare AI with confidential computing
Joint research, collaborative machine learning, pharmaceutical manufacturing. These are only a few applications of AI & ML usage in healthcare. The 4th industrial revolution is at our doorstep, yet many vulnerabilities and security challenges come alongside it. If artificial intelligence is hacked in the learning process, the damage and costs of such breach could soar sky high.
Use case: Collaborative machine learning in Healthcare
This paradigm is built to host high quality models that allow machine learning, based on data from multiple sources. Hospitals need to collaborate and share private and sensitive information and applications, yet some rules must be maintained, to meet the demands of all parties:
- Data is to remain private
- Each entity must have access only to its own data
- Data must not be visible to the cloud provider
- AI code integrity and authenticity must be ensured for the entire training and inference lifecycle
- AI IP know-how must be kept in secret
- High AI performance is required
Our Product: Holistic zero-trust-in-a-box hardware platform
HUB Security’s hardware solution allows a safe environment for machine learning and artificial intelligence processes to take place, while any concern for unwanted visibility between different models. By using Confidential Computing methods we allow for effective collaborative machine learning to take place, thus enabling previously unattainable ventures, in a secure environment.
Our unique Confidential Computing platform supports CPU and GPU of any size, thus enabling all execution environments. During the training process the environment remains under control and thus restricts malicious actors hacking. It is also supported by a physical tamper proof packing material, which adds another needed layer of security.
Learn More About Hub Security Solution for Healthcare
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