Medical Confidentiality and Data Privacy in Machine Learning

Medical organizations, as custodians of protected health information, bear legal and ethical responsibilities. They must prevent unauthorized access to data, which could lead to breaches of confidentiality.

This responsibility, along with the risk of financial and reputational consequences, has created an environment where data custodians are highly reluctant to share patient information or allow access to it.

What We Don’t Do at Guardora?

In MedTech, it is well-known that AI and data privacy enhancement techniques are often applied separately.

Indeed, in many cases, personally identifiable information (PII) is simply anonymized and not used for training ML models. The data used for training ML models generally cannot be traced back to individual patients. In such instances, it's sufficient to clean the textual information containing personal data like names, cities, and test collection sites. There are market solutions available for this kind of anonymization.

What We Do at Guardora?

At Guardora, we specialize in combining these two approaches, where sensitive data must first be protected and then used for training ML models.

There are cases where such protection is required at every stage of data handling:

• Transfer
• Storage
• ML model training
• Quality validation of the trained models
• Model fine-tuning
• Returning results (inference)
• Protecting the models themselves as intellectual property

What Techniques Do We Use?

Healthcare is perhaps the most diverse field in terms of the types of data used. Unstructured medical records, anamnesis notes, diagnoses, dates, numerical, categorical, and binary features, texts, tables, numerical and time series, DICOM format, images, videos, and audio, as well as various ML architectures—all require a combination of various methods and privacy-enhancing computing protocols.

Guardora's solutions are based on employing a wide array of approaches:

• Functional Transformation (Veils)
• Fully Homomorphic Encryption
• Federated Learning
• Functional Encryption
• Secure Multi-Party Computation

Case Studies

Here are several case studies from the healthcare sector that require maintaining the confidentiality of sensitive data while training ML models. These are some of the scenarios we have encountered at Guardora:

• Developing, enhancing, and validating clinical ML algorithms on datasets owned by different entities.
• Securely utilizing data in cost-effective and universal Cloud solutions, as opposed to lengthy, complex, and expensive On-premise implementations.
• Accessing high-quality, diverse datasets representing global patient populations to ensure that algorithms provide equally accurate results regardless of the data collection equipment, patient demographics, clinical settings, or other social factors. To meet this standard, algorithm developers must have access to data that is representative of the scenarios they will encounter during deployment in various clinical environments.
• Protecting intellectual property and ML algorithms of potential competitors during drug discovery research. New technologies, such as CRISPR, are revolutionizing gene editing research for diseases like diabetes and cancer. However, these innovations bring about new security challenges, necessitating full encryption of data even during processing. 
• Human genome data is increasingly being protected as personal information worldwide, making confidential computing with such data potentially a mandatory legal requirement.
• Fetal biometry. Predictive analysis of fetal ultrasound images and videos.
• Analysis, diagnosis, and predictive models for radiology, MRI, and fMRI, including the detection of semiotic signs.
• Text extraction and classification.
• Predictive and diagnostic models that directly interpret the extracted data.
• Predictive analytics.
• Clinical decision support systems.
• Management decision support systems.
• Systems for extracting data from unstructured medical records.
• Systems for creating digital patient profiles.
• Real-world clinical practice research.
• Sale of protected datasets or data enrichment (when Company “A” uploads data and Company “B” receives an enriched segment, or reverse enrichment when Company “C” adds to the benefit of Companies “A” and “B”).
• Linking electronic health records with geolocation as a significant predictor of cancer development, since carcinogens can be geographically specific. Harmful emissions from certain enterprises can increase cancer rates among the local population.
• Telemedicine and remote patient monitoring. Intelligent patient safety monitoring and quality of care assessment using computer vision algorithms.
• Classification and counting of cells in digitized peripheral blood and bone marrow smears.
• Detection of diabetic retinopathy symptoms in fundus images.
• Dental health analysis and monitoring progression.
• Computer vision tasks: segmentation, regression, reconstruction, depending on the type of pathology.

Challenges

Explore the list of current challenges the market needs to address to create high-quality products:

1. Developing an ML solution that integrates data from multiple owners while ensuring the security of each owner's data.
2. Ensuring data security during ML model training outside the trusted environment of the data owner, such as in the Сloud.
3. Ensuring data security during the use (inference) of an ML model deployed outside the trusted environment of the data owner. 
4. Protecting ML models hosted on public resources from unauthorized use and parameter theft.

Not all market participants can guarantee security at the network and physical levels. Therefore, at Guardora, we offer solutions at the algorithmic and protocol levels.

If this topic interests you as a data owner or developer, join our community on Discord and participate in the discussion of these pressing issues.