Distributed AI cloud infrastructure : Privacy Preserving, Real Time

We founded BevelCloud, a distributed AI cloud infrastructure company with a mission to reduce healthcare inequity, lower healthcare costs and improve outcomes for children — locally, rurally and globally by creating and deploying privacy-preserving real-time AI applications based on access to data in all 1,000,000 healthcare machines in all 500 children’s hospitals in the world. For more check out www.pediatricmoonshot.com.

Much like AWS deploys and manages servers in their data centers, BevelCloud deploys and manages cloud servers in the building — the clinic, hospital or research lab. This is all done to enable authorized AI applications to share, infer or learn on standard data anywhere on the planet. We have engineered many unique features to make this possible.

Secure Distributed Compute & Storage

Centralized cloud services today are delivered from a handful of data centers where physical access to the building is strictly enforced. Distributed, edge servers placed in hospitals or clinics cannot realistically require the same level of physical access control. Therefore, unable to rely on physical access control, BevelCloud’s distributed AI infrastructure service implements features necessary to protect the compute & storage in the absence of physical security.

Network Security

One of the main reasons the computing infrastructure needs to be in the building is that the healthcare machines creating the data are in the building, and the only way to communicate with those data-generating machines is to be on the same secure, managed network. So BevelCloud’s network service supports intra-zone (in-the-building) communications, as well as secure extra-zone (outside of the building) communications.

Access to Data from Healthcare Machines

While there is useful data in the electronic medical record (EMR) or electronic health record (EHR), there is far more data in the imaging machines, blood analyzers, drug infusion pumps, ventilators, and gene sequencers. Any in-the-building distributed AI infrastructure cloud service must support access to the static data (e.g., machine serial number), environmental data (e.g., location), dynamic data (e.g., laser power level of the gene sequencer) and finally, the “nomic” data (e.g., echo cardiogram, EEG, MRI scan, gene sequence or blood analysis).

Fine-Grained Data Sharing

One of the fundamentals of privacy is purpose limitation. The distributed in-the-building AI infrastructure service allows for fine-grained data sharing such that a machine owner should be able to choose specific, distributed AI applications with which to share data, as well as which ones not to). Doing so will clearly define not only which data can be shared and with whom, but also for which specific purpose(s) and for commercial and research applications alike.

Distributed AI Application Control

In addition to fine-grained data sharing, the distributed architecture also provides a rigorous process for allowing distributed AI applications in the building. This process includes security vulnerability testing, application security review, and defined whitelists for any external communication.

Image Sanitization

As one of the privacy management features BevelCloud provides an image sanitization service This enables any AI application to automatically identify and redact any personally identifying information (PII) present on the images.

Inference Services

The architecture supports real-time AI inference. Regardless of where an AI application’s training takes place (using a distributed or a centralized architecture), the servers at the point of care must ultimately be able to execute locally on that learning (i.e., execute the resulting AI application) without having to rely on or make use of servers outside of the building.

Federated Learning Services

Finally, the distributed AI infrastructure service is optimized for privacy-preserving, network-preserving, federated learning. Rather than a centralized architecture that learns on 6,000,000 terabytes of shared, aggregated ultrasound data in a central site, federated learning would allow the AI training to take place in a distributed fashion across the 7,000 servers located in all 500 children’s hospitals around the globe.

We need a new infrastructure, but where will the applications come from? Read on.