Top 5 Confidential IT Uses in Healthcare

Big data meets private data in a perfect storm for healthcare. Confidential computing vendors say they will make the cloud safer for medical data.

A medical professional is typing on a computer with medical equipment in the foreground. — Image: National Cancer Institute/Unsplash

Medical information is personal and private. For both legal and ethical reasons, it is essential that this remains so. Government regulations like HIPAA have been in the news lately, but tech companies are still exploring how to implement them.

Many companies try to condition privacy in different ways. Confidential computing is an initiative often talked about in the same breath as personal and patient privacy and has become a new frontier for cloud providers.

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Confidential Computing aims to protect data while it is in transit, in use, and at rest, combating attackers who use memory scraping to infiltrate data in use. It can involve artificial intelligence or machine learning and can work with traditional servers or virtual machines, but the definition is broad enough to include many different tools and approaches. It is often a reliable execution environment that protects data from outside influences.

Confidential computing also allows AI algorithm developers to share large datasets without sharing IP. This is often where it intersects with healthcare, as patient information and large shared black box datasets would otherwise be a tricky combination. Confidential computing has several applications in the field of health.

Top 5 Use Cases for Confidential Computing in Healthcare

1. Protect against cyberattacks

In general, confidential computing is a new way of thinking about data protection. Protecting private patient information is a top priority for hospitals and other healthcare organizations to maintain trust and comply with government regulations.

Meanwhile, attackers have started targeting data on the move. Microsoft Azure shows how TLS encryption and attestation are used to protect patient information, run machine learning on sensitive information, or run algorithms on encrypted datasets from many sources without opening doors for attackers . It reduces the attack surface visible from the outside.

Fortanix demonstrates the use of confidential computing in healthcare security with its adoption of Intel Software Guard extensions. This creates a hardware-based TEE or memory “enclave” around the computer where the AI workload is isolated and processed. This enclave exists entirely separately from the host operating system, hypervisor, root user, and peer applications running on the same processor.

We’ll have more to say about AI later, but confidential computing is also being applied to outrun attacks on IoT medical devices and cloud data.

2. Comply with industry regulations

Confidential IT Services are well aware of the many industry regulations regarding customer data. For example, HIPAA sets specific rules for cloud computing.

IBM says it built this understanding into confidential computing from the start. Their Hyper Protect iOS SDK for Apple CareKit encrypts data for the open source health app development platform. It can be used for dynamic care plans, symptom tracking, and connecting to care teams, which may involve moving sensitive personal information from one location to another during healthcare work.

3. Securing AI research

Healthcare workers can use AI to help nurses and doctors with daily tasks, analyze large amounts of data to improve early disease detection through pattern recognition, monitor heart conditions and train professionals of health. Naturally, there is a fear of creating huge volumes of data in a very private setting. Confidential computing can contribute to this.

Recently, Microsoft partnered with BeeKeeperAI to allow AI developers to access it through the Azure confidential computing environment.

“The potential for AI to enable the delivery of better healthcare outcomes continues to grow exponentially, but developers are limited by access to critical datasets to train and deploy their algorithms,” said John Doyle, global chief technology officer at Microsoft, in a press release. release of BeeKeeperAI. “We are thrilled to partner with BeeKeeperAI to help the healthcare industry develop the understanding and expertise it needs to leverage confidential computing in healthcare innovation.”

4. Secure contact tracing

Contact tracing has become a common phrase after COVID-19. Intel notes that confidential computing – blockchain-based, in this case – is the backbone of MicrobeTraceNext, an AI project done in collaboration with Intel and Leidos.

Two blockchain keys and a role-based security check protect PII. Intel Xeon Scalable processor platforms enable ledger-based encryption, making all data access and data movement fully auditable and traceable and all transactions immutable. Confidential computing enhances secure contact tracing on a regional or national level.

5. Secure medical imaging

Intel also noted that medical imaging can benefit from confidential computing. They contributed Intel Xeon Scalable processors and AI acceleration for Federated Learning, a privacy project that allowed three hospitals to share a common AI model without sharing PII. Each hospital trained their AI model locally, then aggregated that data on a central server in the cloud. The aggregation allowed the model to improve based on the three hospitals.

No patient information or intellectual property of the AI model itself was shared. This distinction was made possible by Intel’s Confidential Computing. The AI model, which was trained to diagnose medical images, was learning from the three hospitals while shielded from outside eyes.