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Glossary

Confidential Computing

Confidential computing is a cloud security model that uses hardware-based Trusted Execution Environments (TEEs) to isolate and protect data during processing, keeping it inaccessible to the cloud provider, OS, and other applications.
Chainscore © 2026
definition
SECURITY PRIMITIVE

What is Confidential Computing?

Confidential computing is a hardware-based security model that protects data in use by isolating it within a protected CPU enclave during processing.

Confidential computing is a hardware-enforced security model that protects sensitive data during computation by isolating it within a Trusted Execution Environment (TEE). Unlike traditional encryption, which secures data at rest (in storage) and in transit (over a network), confidential computing addresses the final vulnerability: data in use. A TEE is a secure, isolated region within a central processing unit (CPU) that is cryptographically shielded from the rest of the system, including the host operating system, hypervisor, and even cloud service providers. This ensures that code and data loaded into the enclave are protected from external observation or modification.

The core mechanism relies on remote attestation, a cryptographic process that allows a client to verify the integrity and authenticity of the TEE and the application (attested enclave) running inside it before sending any sensitive data. This proves that the software is unaltered and running on genuine, certified hardware. Major CPU manufacturers provide TEE implementations, such as Intel SGX (Software Guard Extensions), AMD SEV (Secure Encrypted Virtualization), and ARM TrustZone. These technologies create a root of trust anchored in the processor's silicon, enabling computations on encrypted or otherwise sensitive data without exposing it in plaintext to the broader system memory.

In practice, confidential computing enables multi-party computation and privacy-preserving analytics, where multiple organizations can jointly analyze their combined datasets without revealing their individual proprietary information. It is foundational for secure cloud computing, allowing tenants to run workloads on shared infrastructure with the assurance that the cloud provider cannot access their data. Use cases extend to protecting AI/ML models and training data, securing blockchain smart contract execution, and facilitating regulatory compliance in industries like healthcare and finance by enabling data processing in previously prohibited environments.

how-it-works
MECHANISM

How Confidential Computing Works

Confidential computing is a hardware-based security model that protects data during processing by isolating it within a protected CPU enclave.

Confidential computing works by leveraging hardware-based Trusted Execution Environments (TEEs), such as Intel SGX, AMD SEV, or ARM TrustZone. A TEE is a secure, isolated region within the main processor, often called an enclave. Data and code loaded into this enclave are encrypted in memory and are inaccessible to the host operating system, hypervisor, cloud provider, or any other process—even those with root or administrative privileges. This ensures that sensitive information remains protected not just at rest (in storage) and in transit (over a network), but also in use during computation.

The process begins when an application designates a sensitive portion of its code and data to run inside the TEE. The hardware encrypts this memory region with a key unique to the CPU and the specific enclave. Any attempt to read this memory from outside the enclave returns only ciphertext. The integrity of the enclave is cryptographically verified through a process called remote attestation, which allows a client to cryptographically verify that their code is running in a genuine, unaltered TEE on a specific platform before sending any sensitive data to it.

This architecture enables new paradigms for secure multi-party computation. For example, multiple parties can jointly analyze a dataset—such as combining medical records from different hospitals for research—without any single party, including the cloud infrastructure provider, ever seeing the raw, unencrypted data. The computation occurs solely within the protected enclave. This resolves the traditional security trade-off in cloud computing, where users had to trust the cloud provider's software stack and personnel with their data during processing.

key-features
CORE MECHANISMS

Key Features of Confidential Computing

Confidential computing is a security paradigm that protects data in use by isolating computation within a hardware-based trusted execution environment (TEE).

01

Hardware-Based Trusted Execution Environment (TEE)

The foundational component of confidential computing. A Trusted Execution Environment (TEE) is a secure, isolated area within a main processor, created using hardware extensions like Intel SGX, AMD SEV, or Arm CCA. It ensures that code and data loaded inside are protected with confidentiality, integrity, and attestation, even from the host operating system, hypervisor, or cloud provider.

02

Remote Attestation

A cryptographic protocol that allows a client to verify the integrity and authenticity of the software running inside a remote TEE. It proves that:

  • The correct application is running.
  • It is executing within a genuine, secure hardware enclave.
  • The initial state (code and data) has not been tampered with. This enables trusted deployment of sensitive workloads in untrusted environments like public clouds.
03

Data Confidentiality & Integrity

Data is encrypted while in memory and is only decrypted inside the secure CPU boundary of the TEE. This provides:

  • Confidentiality: Prevents unauthorized read access, including from privileged system software and physical attacks like cold-boot attacks.
  • Integrity: Ensures data cannot be altered outside the TEE without detection. Any unauthorized modification triggers an integrity failure, halting execution.
04

Secure Enclave Page Cache (EPC)

A critical memory management feature in TEEs like Intel SGX. The Enclave Page Cache (EPC) is a reserved, encrypted region of physical memory that stores pages belonging to an enclave. The Memory Encryption Engine (MEE) transparently encrypts/decrypts data moving between the CPU and EPC, ensuring data is only in plaintext within the CPU itself.

05

Sealing & Provisioning

Key management operations specific to TEEs:

  • Sealing: The process of encrypting data by the enclave, using a key derived from the enclave's identity and the platform hardware, for persistent storage. Only the same enclave (or a designated successor) on the same platform can unseal it.
  • Provisioning: The secure process of delivering secrets (e.g., keys, credentials) into an attested enclave, often via a trusted service.
06

Use Cases & Examples

Confidential computing enables new trust models for multi-party collaboration:

  • Privacy-Preserving Analytics: Joint analysis of sensitive datasets (e.g., healthcare, finance) without exposing raw data.
  • Secure AI/ML: Training models on encrypted data or protecting proprietary model IP during inference.
  • Blockchain & DeFi: Enabling private smart contracts and confidential transactions (e.g., Oasis Network, Secret Network).
  • Digital Rights Management (DRM): Protecting high-value media content during playback.
primary-use-cases
CONFIDENTIAL COMPUTING

Primary Use Cases

Confidential computing enables data to be processed in a secure, isolated environment, ensuring its confidentiality and integrity even from the infrastructure provider. This unlocks applications where privacy and security are paramount.

01

Secure Multi-Party Computation (MPC)

Enables multiple parties to jointly compute a function over their private inputs without revealing those inputs to each other. This is foundational for:

  • Privacy-preserving analytics on sensitive datasets from multiple organizations.
  • Private voting and governance mechanisms.
  • Fraud detection across banks without sharing customer transaction data.
02

Private Smart Contracts & DeFi

Protects sensitive on-chain transaction data, such as trade amounts, wallet balances, and user identities, while maintaining blockchain verifiability. Key applications include:

  • Private decentralized exchanges (DEXs) that hide order sizes to prevent front-running.
  • Confidential lending where collateral and loan details are encrypted.
  • Private auctions and sealed-bid processes.
03

AI Model & Data Privacy

Secures proprietary AI models and training data during inference and training. This allows:

  • Model-as-a-Service where the model owner's intellectual property is protected from the client, and the client's data is protected from the provider.
  • Federated learning with enhanced security guarantees, ensuring participant data never leaves its secure enclave.
  • Compliance with data sovereignty regulations (e.g., GDPR, HIPAA) in cloud environments.
04

Digital Rights Management (DRM) & Content Protection

Creates a trusted execution environment for decrypting and rendering premium content (e.g., 4K video, software licenses) on untrusted user devices. It ensures:

  • End-to-end content protection from server to display.
  • Secure license enforcement and key management.
  • Prevention of unauthorized copying, screen recording, or tampering.
05

Supply Chain & Intellectual Property

Enables secure collaboration between companies in a supply chain while protecting sensitive business logic and data. Use cases include:

  • Verifying component provenance without exposing full supplier lists or pricing.
  • Secure sharing of CAD files or manufacturing specs with partners.
  • Auditable compliance checks (e.g., for conflict minerals) using private data.
06

Healthcare & Genomic Data Analysis

Allows researchers and institutions to perform computations on highly sensitive patient data (genomic, medical records) without exposing the raw data. This facilitates:

  • Cross-institutional medical research while preserving patient privacy.
  • Personalized medicine where analysis is done on encrypted patient data.
  • Secure aggregation of health data for public health monitoring.
CONFIDENTIAL COMPUTING

Data Protection States: A Comparison

A comparison of how data is protected at different stages of its lifecycle within a compute environment.

Protection StateData at RestData in TransitData in Use

Definition

Data stored on a persistent medium (e.g., disk, SSD).

Data actively moving between systems or components.

Data being actively processed in CPU and memory.

Primary Threat

Physical theft, unauthorized disk access.

Eavesdropping, man-in-the-middle attacks.

Memory scraping, side-channel attacks, malicious host.

Traditional Control

Full-disk encryption, access controls.

TLS/SSL encryption (e.g., TLS 1.3).

OS-level process isolation, user permissions.

Confidential Computing Control

Encryption with hardware-rooted keys (e.g., TPM).

End-to-end encrypted channels into secure enclaves.

Hardware-based Trusted Execution Environment (TEE).

Hardware Root of Trust

Trusted Platform Module (TPM), HSM.

Not typically applicable at this layer.

CPU Security Extensions (e.g., Intel SGX, AMD SEV, Intel TDX).

Attestation Scope

System integrity measurement at boot.

Channel and endpoint identity verification.

Runtime integrity verification of the TEE and its initial state.

Data Exposure Risk

Low (if encrypted with secure keys).

Low (with strong transport encryption).

High (in traditional systems); Very Low (within a verified TEE).

key-technologies-standards
CONFIDENTIAL COMPUTING

Key Technologies & Standards

Confidential Computing is a hardware-based security paradigm that isolates sensitive data and code execution within a protected CPU enclave during processing, shielding it from other software, the operating system, and even cloud providers.

02

Remote Attestation

Remote Attestation is a cryptographic protocol that allows a remote verifier to confirm the identity and integrity of a TEE and the application running inside it. It proves that:

  • The code is running on genuine hardware.
  • The software stack is in a known, unaltered state.
  • The enclave's measurements match a trusted source. This enables secure provisioning of keys and sensitive data from external services.
03

Intel SGX (Software Guard Extensions)

Intel Software Guard Extensions (SGX) is a set of CPU instructions that create hardware-isolated enclaves within an application's address space. Key features include:

  • Memory Encryption: Enclave data in RAM is encrypted.
  • Access Control: The CPU enforces access, preventing even privileged OS or hypervisor code from reading enclave memory.
  • Sealing: Data can be encrypted for persistent storage, bound to the specific enclave and platform. It's a foundational TEE technology for cloud and blockchain applications.
04

AMD SEV (Secure Encrypted Virtualization)

AMD Secure Encrypted Virtualization (SEV) is a hardware feature that encrypts the memory of individual virtual machines (VMs) with a unique key. Unlike per-application enclaves (SGX), SEV protects entire VMs, making it suitable for confidential VMs in cloud environments. The hypervisor manages the VMs but cannot access their encrypted memory contents, providing isolation from the cloud provider.

05

Enclave & Application Partitioning

A core principle of Confidential Computing is partitioning an application into a trusted and an untrusted component.

  • Trusted Component: Runs inside the secure TEE, handling sensitive logic (e.g., private key operations, model inference).
  • Untrusted Component: Manages non-sensitive tasks, networking, and UI outside the enclave. This minimizes the trusted computing base (TCB), reducing the attack surface. Communication between parts occurs via a controlled interface.
blockchain-web3-application
APPLICATION

Confidential Computing in Blockchain & Web3

Confidential Computing is a hardware-based security paradigm that processes encrypted data in a protected CPU enclave, enabling privacy-preserving smart contracts and decentralized applications.

Confidential Computing is a hardware-enforced security model that allows data to be processed while remaining encrypted, even in memory. In blockchain and Web3, this is achieved through Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV, which create isolated, cryptographically sealed enclaves within a processor. This enables privacy-preserving smart contracts to execute complex logic on sensitive inputs—such as personal identity data, proprietary trading algorithms, or private financial records—without exposing the raw data to the node operators, validators, or even the underlying blockchain itself.

The primary application in decentralized networks is to resolve the scalability-privacy paradox. Traditional blockchains like Ethereum make all contract state and computation public, which is unsuitable for enterprise or regulated use cases. Confidential computing architectures, such as those implemented by Oasis Network or Secret Network, use TEEs to perform computations off-chain in a verifiable manner. The blockchain only records encrypted inputs and outputs, along with a cryptographic attestation that the computation was performed correctly within the secure enclave, thus maintaining data confidentiality and computational integrity simultaneously.

Key use cases powered by this technology include private decentralized finance (DeFi) for hidden order books and confidential transactions, secure data marketplaces where analysis can be performed on encrypted datasets, and identity management systems that verify credentials without revealing them. Furthermore, it enables cross-chain bridges and oracles to handle sensitive data feeds privately, and facilitates regulatory compliance (like GDPR) by ensuring personal data is never exposed in cleartext on a public ledger, making blockchain viable for a much broader range of real-world applications.

benefits
CONFIDENTIAL COMPUTING

Core Benefits

Confidential Computing is a hardware-based security technology that isolates sensitive data and code within a protected CPU enclave during processing. Its primary benefits include:

01

Data Confidentiality in Use

Protects sensitive data while it is being processed in memory, closing the final gap in the data lifecycle (at rest, in transit, and now in use). This is achieved through Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV, which encrypt data within the CPU and prevent access from the host operating system, hypervisor, or cloud provider.

02

Code Integrity

Ensures that application code executes exactly as intended, without being tampered with. The TEE cryptographically verifies the code's identity and measurements before allowing it to run inside the secure enclave, guaranteeing integrity and preventing unauthorized modifications or malware injection.

03

Secure Multi-Party Computation

Enables multiple parties to jointly compute a function over their private inputs without revealing those inputs to each other. This is foundational for use cases like:

  • Private smart contracts where transaction details are hidden.
  • Cross-organizational data analysis (e.g., fraud detection) without sharing raw data.
  • Sealed-bid auctions where bids remain secret until the winner is determined.
04

Regulatory & Compliance Enabler

Facilitates compliance with data protection regulations like GDPR, HIPAA, and CCPA by providing a technical mechanism to process regulated data in untrusted environments (e.g., public clouds). It allows data controllers to demonstrate that third-party processors cannot access the plaintext data, satisfying requirements for data minimization and security.

05

Enhanced Cloud Adoption

Removes a major barrier to cloud migration for sensitive workloads. Organizations can leverage public cloud scalability and cost benefits for processing Intellectual Property (IP), financial records, or healthcare data, with the assurance that the cloud provider cannot see or exfiltrate the data from the secure enclave.

CONFIDENTIAL COMPUTING

Security Considerations & Challenges

Confidential computing is a security paradigm that isolates sensitive data and code execution within a hardware-protected enclave, shielding it from other software, the host operating system, and even cloud providers. This section addresses the core questions and challenges surrounding its implementation in blockchain and Web3.

Confidential computing is a security model that uses hardware-based Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV to create isolated, encrypted memory regions called enclaves. Within an enclave, code and data are protected from external access, even from privileged system software or the cloud infrastructure provider. The process works by: 1) loading encrypted data into the enclave, 2) decrypting it only inside the secure CPU boundary, 3) performing computations on the plaintext data, and 4) outputting encrypted results. This ensures data remains confidential and its integrity is verifiable via remote attestation, which cryptographically proves the code is running in a genuine, unaltered enclave.

CONFIDENTIAL COMPUTING

Frequently Asked Questions (FAQ)

Confidential computing is a security paradigm that protects data in use by isolating it within a hardware-based trusted execution environment. This section answers common questions about its role in blockchain and Web3.

Confidential computing is a security model that protects data in use by executing computations within a hardware-isolated, attested environment called a Trusted Execution Environment (TEE). It works by creating an encrypted, tamper-proof enclave on a processor (e.g., using Intel SGX or AMD SEV) where sensitive data is decrypted, processed, and re-encrypted, remaining invisible to the host operating system, cloud provider, or any other privileged software. This ensures that even if the underlying system is compromised, the data and the computation logic remain confidential and verifiably correct.

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Confidential Computing: Definition & Key Features | ChainScore Glossary