status.health

Trusted Execution Environments: Hardware-Based Privacy for Health Verification

Every CISO knows the nightmare: that 2 AM call about a health data breach. Your vendor stored vaccination records. Your stock drops 12%. Your CEO testifies before Congress. The architecture of health verification systems guarantees this outcome because verification has always meant possession. Until now.

Trusted Execution Environments weaponize your processor against itself. The CPU encrypts memory at the bus level using keys burned into silicon during fabrication. Your operating system becomes just another untrusted application. Even kernel exploits and hypervisor escapes hit cryptographic walls. Combining TEEs with zero-knowledge proofs makes health verification possible with zero risk of exposure, compliance issues, or liability.

Hardware Trust Inversion

Traditional security models treat the kernel as the ultimate authority. Ring 0 controls everything. One privilege escalation vulnerability hands attackers complete system control. TEEs invert this hierarchy by making hardware distrust its own software.

Intel’s approach burns fuse keys into processors during manufacturing. These keys physically cannot be extracted through software. The memory encryption engine sits between CPU cores and DRAM, transparently encrypting cache lines with ephemeral keys derived from the root secrets. Attempts to read enclave memory from privileged code return only ciphertext. Physical attacks face the economics problem: extracting keys from one chip through decapping and electron microscopy might be possible, but scaling such attacks across millions of devices remains infeasible.

ARM built TrustZone differently. Rather than encrypting memory, they created two parallel processor states. The Secure World runs its own operating system with hardware-enforced isolation. A completely compromised Android kernel cannot access Secure World memory because the processor itself enforces the boundary. Apple extended this concept by building dedicated security processors. The Secure Enclave exists as separate silicon with independent boot ROM, random number generation, and cryptographic engines.

AMD’s SEV technology encrypts entire virtual machines with unique per-VM keys. The hypervisor can schedule and migrate these VMs but cannot decrypt their memory. Cloud providers become blind infrastructure operators. They provide compute cycles without visibility into computations.

The economic beauty of hardware security lies in its asymmetry. Attackers might compromise individual devices through extraordinary effort, but they cannot compromise the manufacturing process at scale without detection. They cannot retroactively decrypt data processed in past TEE sessions. The threat model fundamentally shifts from exploiting any software vulnerability to requiring physical access to specific chips.

Architectural Paths for Health Verification

Research into TEE-based health verification reveals three promising architectural approaches, each addressing different technical constraints and use cases.

Visual Processing Without Persistence

The computer vision approach would stream video directly into TEE memory for real-time analysis without frame storage. WebRTC could deliver 30 frames per second to machine learning models running entirely within protected memory. The core challenge involves extracting text from health documents accurately while ensuring zero data persistence.

Memory constraints create the primary obstacle. Intel SGX enclaves support maximum 256MB of protected memory. Modern OCR models typically require gigabytes for optimal accuracy. Model quantization research suggests 4-bit precision reduces memory requirements by 8x while maintaining acceptable accuracy on structured documents. Custom memory allocators would need to cryptographically erase all data immediately after processing.

Real-world conditions compound the difficulty. Users move phones while capturing documents. Overhead lighting creates glare and shadows. Documents curl at edges. Achieving reliable text extraction under these conditions without storing reference frames presents a fundamental computer vision challenge requiring extensive research and novel approaches.

Ephemeral API Integration

Health system APIs assume persistent connections and token storage. OAuth2 flows expect refresh tokens to survive across sessions. TEE architectures break these assumptions through memory volatility and size constraints. Encrypted memory disappears on power loss. Enclave restarts regenerate all keys. Traditional session management becomes impossible.

The architectural challenge involves reimagining authorization for ephemeral environments. Each API interaction would operate as a complete, stateless transaction. Health systems like Epic’s FHIR endpoints expect persistent connections for bulk data operations. A TEE-based approach must maintain minimal state while processing potentially thousands of records. Rate limiting without persistent storage could leverage probabilistic data structures like bloom filters sealed within enclave memory.

This approach offers superior security properties. Compromised tokens vanish with enclave termination. Credential theft becomes meaningless when credentials exist only momentarily. However, engineering complexity multiplies as developers must handle retry logic, connection management, and error recovery without traditional state persistence.

Document Intelligence Under Constraints

PDF processing exemplifies the resource challenges facing TEE-based document analysis. The format supports embedded JavaScript, custom fonts, complex layouts, and multimedia content. Parser vulnerabilities regularly appear in CVE databases. Medical documents add specialized formatting, dense information layouts, and critical accuracy requirements.

TEE memory limitations force radical architectural changes. Traditional PDF libraries allocate hundreds of megabytes for rendering complex documents. A streaming parser operating in 8KB chunks could extract text without full document materialization. Font rendering presents particular challenges since medical documents use specialized typefaces for clarity. Pre-rendering common medical fonts into signed bitmap atlases trades flexibility for security.

The intelligence layer faces similar constraints. Transformer models for document understanding typically require gigabytes of memory. Quantization to 4-bit precision reduces model size while maintaining accuracy on structured forms. Handwritten clinical notes remain especially challenging, requiring either breakthrough compression techniques or architectural innovations to fit within TEE constraints.

Proof Generation Within Silicon Boundaries

Zero-knowledge proofs enable mathematical verification without data disclosure. The theory is elegant. Implementation within TEE constraints proves brutal.

Consider the challenge of proving that a lab result shows negative status within the past 30 days. The proof system must parse the document, extract relevant fields, validate digital signatures, compare dates, and produce a binary output. Each operation translates into arithmetic constraints within large equation systems. The prover generates a mathematical proof that these constraints were satisfied without revealing the input values.

Modern zkVM frameworks assume gigabytes of memory for proof generation. RISC Zero, a leading framework, typically requires 4-8GB for complex proofs. TEE environments offer 256MB at most. This gap represents a fundamental engineering challenge. Progressive proof generation offers one path forward: generate smaller proofs for individual claims, then recursively combine them into a comprehensive proof.

Different proof systems offer distinct tradeoffs. Groth16 produces remarkably compact proofs, often under 200 bytes, with millisecond verification times. However, it requires a trusted setup ceremony. STARK-based systems eliminate setup requirements but generate larger proofs, often several kilobytes. The choice depends on specific deployment constraints and trust models.

Regulatory Compliance Through Architecture

HIPAA’s scope depends on specific activities involving protected health information, namely creating, receiving, maintaining, or transmitting such data. Organizations performing these activities become covered entities or business associates with extensive compliance obligations. Technical architecture that makes these activities impossible removes organizations from HIPAA’s scope entirely.

This represents a fundamental shift from policy-based to architecture-based compliance. Traditional health tech companies accept PHI and implement elaborate controls. They manage access through role-based permissions. They encrypt data at rest and in transit. They maintain audit logs and conduct security training. Despite these measures, breaches occur regularly because the data exists to be breached.

Architecture that processes health data exclusively on user devices within TEEs creates a different reality. The service provider never receives health information because the network only carries cryptographic proofs. These proofs state “verification passed” without revealing underlying data. Courts and regulators recognize that mathematical proofs differ categorically from medical records.

The implications cascade through other regulations. GDPR’s Article 9 prohibits processing special category health data. Processing confined to user-controlled devices falls outside this prohibition. California’s medical privacy acts trigger on health data disclosure. Zero-knowledge proofs disclose computational results, not health data. International data transfer restrictions govern medical records, not mathematical proofs.

Performance Realities and Tradeoffs

TEE overhead varies by architecture and workload. Intel SGX documentation indicates 5-15% performance degradation for memory-intensive operations due to encryption overhead. Context switches between trusted and untrusted code cost 8,000-20,000 processor cycles. These fundamental costs shape architectural possibilities.

ARM TrustZone exhibits different characteristics. World switches cost fewer cycles but require complex state management. The Secure World typically runs a minimal operating system, limiting available functionality. Apple’s Secure Enclave demonstrates the dedicated processor approach with consistent performance but limited general computation capabilities.

Zero-knowledge proof generation adds substantial computation. Academic benchmarks show proof generation times ranging from seconds for simple statements to minutes for complex proofs. Mobile processors face additional constraints from thermal throttling and battery consumption. Users may wait 30-60 seconds for complex health verification proofs.

These performance realities demand careful user experience design. Progress indicators must accurately reflect proof generation stages. Architectures should minimize repeated proofs through careful caching of proof artifacts rather than source data. Background proof generation could improve perceived performance while maintaining security properties.

Market Forces Driving Transformation

Healthcare data breaches reached unprecedented levels in 2024. HHS reported over 700 breaches affecting more than 150 million individuals. IBM’s Cost of a Data Breach report pegs healthcare breach costs at $10.93 million on average, 2.5x the global average across industries. These economics make traditional approaches unsustainable.

Enterprises need health verification across numerous use cases ranging from employers verifying vaccination status for return-to-office policies to insurers confirming wellness activities for premium adjustments. Clinical trials must validate participant eligibility while dating platforms seek STI status verification, yet current solutions force all these organizations to accept health data custody and its associated liabilities.

Major technology companies recognize the opportunity with Apple positioning privacy as a core differentiator through exclusive on-device health data processing. Google Cloud promotes confidential computing for sensitive workloads while Microsoft Azure offers multiple TEE options for enterprise customers. The infrastructure exists today alongside market demand for solutions and a regulatory environment that increasingly punishes data aggregation.

Engineering the Path Forward

status.health’s research indicates that combining TEEs with zero-knowledge proofs can fundamentally restructure health verification. The architecture would enable mathematical certainty without data custody. Enterprises could verify health actions without regulatory exposure. Users would maintain complete control while enabling necessary verifications.

Significant technical challenges remain in this approach. The constraints of TEE environments demand architectural innovations to fit complex computations within limited memory. Battery-powered devices require careful optimization to achieve acceptable performance while developer adoption depends on building abstractions that hide cryptographic complexity without sacrificing security.

Success demands unwavering commitment to zero-data principles. The temptation to accept “just metadata” or implement “temporary caching for debugging” must be absolutely resisted. Technical architecture must enforce business discipline. When storing data becomes impossible, privacy becomes inevitable.

The transformation will require excellence across multiple domains, from deep understanding of TEE architectures and constraints to expertise in proof system optimization. API design must balance accessibility with security guarantees while maintaining absolute clarity that health data belongs to users alone.

The technological pieces exist today with TEEs shipping in billions of devices and zero-knowledge proofs achieving practical performance for targeted use cases. The market desperately needs solutions while regulatory pressure intensifies annually, creating unprecedented opportunity for those willing to architect systems where verification requires zero trust and zero data.

status.health will build infrastructure making health verification as simple and safe as any other API call through hardware-enforced privacy and mathematical verification that achieves compliance through technical impossibility. Your health data should verify everything while revealing nothing.

Notes

The research here is indicative of the complexity of health verification but it is a task worth undertaking. If you’re curious to learn more about status.health and our technology, or about zkVMs and TEEs, you can subscribe to our newsletter, or check out the Learn section of our website.

For any questions, reach out to info@status.health.

— s.h

Health fact: "Regular A1C testing every 3 months for diabetics reduces complications by 40% through better glucose management."