Medical Privacy in the Search for a Cure for Cancer

In today’s digital landscape, privacy has become a top concern. With data breaches making headlines, the fear of losing control over our personal information is stronger than ever. The recent US Healthcare data breach, affecting 100 million people, is a daunting example of our vulnerability: our medical records, passwords, financial data, and identity – are all at risk.

This is even more true when it comes to protecting our medical and biometric data, perhaps because unlike financial information, which can be protected or, if necessary, reset, one’s biometric data is indelible and permanent, making its protection vital. While governments have taken massive steps forward to ensure our medical privacy through regulation, such efforts do not address the technological advancements that threaten our personal data.

The other side of that technology offers promise for a better world. The combination of high-performance computing and artificial intelligence (AI) has advanced analytical capabilities to enable solutions and treatments for many of today’s most difficult medical challenges. The biggest impediment to such analytical breakthroughs is a lack of real-world data.

The Promise of AI for Healthcare Innovation

AI has the potential to revolutionize healthcare, but it needs vast amounts of data to uncover patterns that indicate diseases early in the process. Given enough information, AI can identify subtle warning signs for cancer or heart disease. For instance, if AI analytics are applied to isolate a pattern of test results that indicates early onset of heart disease, researchers can then watch for that pattern and possibly provide preventative medications to slow or even avoid that onset.

In another example, Google uses AI to analyze DNA samples for specific genomic patterns that indicate a likelihood of developing a certain type of cancer. The NIH uses AI to analyze DNA to link the best medications to the patient’s specific genetic makeup for optimal results. With enough data to work with, it won’t be long before AI could very well identify an actual cure for many cancers.

This leads to a critical question: how can we harness the power of AI for medical advancements without compromising our privacy? To fuel these advancements, AI requires vast amounts of real-world data. But sharing medical information creates an immense risk to our privacy. A new technology is needed – one that protects personal data while enabling incredible medical innovations.

The Real Danger to Our Data Privacy

The single greatest threat to our privacy is data breaches on unencrypted data. While hackers are adept at accessing secure servers, they are mostly powerless against encryption, which keep the data within those servers securely protected. Data breaches and leaks are problematic because data was either stored, transferred, or processed while unencrypted.

While the solution might seem as simple as applying encryption to data at all stages (at rest, in transit, in use), the truth is that today it is impossible to process encrypted data at scale.  Therefore, any medical data that you share for processing, whether with a lab for analysis, an insurance company to receive a quote, or an academic institute for medical research, is highly likely to be unencrypted at some point to enable it to be processed.

The Case for Fully-Homomorphic Encryption

One technology that has been developed to address exactly this issue is Fully-Homomorphic Encryption (FHE). FHE is a cryptographic operation that enables data to be processed without it ever needing to be unencrypted, comparable to a locked vault in which data can be analyzed but not accessed. That means that the privacy of the data is preserved no matter who uses it and no matter what they are using it for.

Suppose, for instance, that an AI machine learning model needs millions of patient records to accurately identify the genetic patterns of a certain type of cancer. Today, providing such information would require patient consent, reducing the likelihood of acquiring enough records for an accurate analysis. It would also be impossible today, using traditional encryption methods, to access and analyze these records without having to decrypt them. That would mean that patient identities and medical records are vulnerable.

With FHE, though, data remains encrypted at all times and at every stage, eliminating the need for the cumbersome patient consent process. Encrypted medical information, even if it does end up in the wrong hands, is gibberish unless it is decrypted. The benefit is twofold: it accelerates medical innovation by providing secure access to valuable data while ensuring that patient privacy is maintained.

The Challenge of Applying FHE at Scale

While FHE already exists and can be applied on a small scale for specific use cases, it cannot yet be implemented at the scale required to enable such medical breakthroughs. This is because there is significant computational overhead involved in the complex cryptographic calculations that are required for processing encrypted data. It is estimated that applying FHE to a cloud-based AI model for analyzing millions of records would require nearly a million times the processing power of today’s processors.

That is why much effort is being made by both government-funded projects, such as DARPA’s DPRIVE, and private corporate efforts, such as  Chain Reaction’s 3PU™ privacy processor, to develop a hardware-based accelerator that can implement FHE at scale. Once the processor exists to overcome the computational overhead, the possibilities for privacy-safe medical advancement are endless.

Once FHE is adopted, all our privacy concerns will be assuaged. Our personal data will remain encrypted at all times, unable to be accessed even if it were to be hacked or leaked. And just as importantly, by removing the privacy hurdle, we will open a new world of medical research and innovation, enabling us to live both healthier and more securely.