Confidential Computing

• Confidential computing is a hardware-rooted approach that uses Trusted Execution Environments (TEEs) and cryptographic protocols to secure data during active processing.
• It relies on mechanisms like remote attestation, hardware root-of-trust, and memory encryption to maintain confidentiality even against malicious system software.
• Practical applications span cloud HPC, edge AI, and privacy-preserving analytics, though challenges include performance overhead and side-channel vulnerabilities.

Confidential computing is a set of architectural and cryptographic techniques that guarantees the confidentiality and integrity of data and computation “in use,” typically by isolating sensitive code and memory in hardware-protected regions called Trusted Execution Environments (TEEs). Unlike classical security which targets data at rest and in transit, confidential computing addresses the data-in-use threat surface—even in the presence of malicious operating systems, privileged host software, or hostile operators—by enforcing hardware-rooted isolation, attestation, and verifiable computation. The Linux Foundation’s Confidential Computing Consortium has formalized the paradigm as “the protection of data in use by performing computation in a hardware-based, attested TEE” (Mo et al., 2022).

1. Architectural Foundations and Core Threat Model

Confidential computing rests on several foundational hardware security elements:

• Trusted Execution Environments (TEEs): On-chip hardware enclaves (e.g., Intel SGX, AMD SEV, ARM TrustZone, Blackwell confidential GPUs, RISC-V CoVE) isolate an enclave’s code and memory from all other software and most physical attacks (Mo et al., 2022, Sahita et al., 2023).
• Hardware Root-of-Trust (RoT): Boot-time measurement and fuse-stored secrets ensure that only authorized firmware and enclave images are loaded, and provide attestation (Sahita et al., 2023, Shang et al., 5 Dec 2024).
• Remote Attestation: TEEs produce cryptographic evidence (e.g., MRENCLAVE, signed reports) that their loaded code is authentic and corresponds to pre-agreed measurements. Clients verify attestation before provisioning secrets (Chen, 2022, Sahita et al., 2023).
• Encrypted Memory: Hardware memory encryption engines (e.g., Intel SGX’s Memory Encryption Engine, AMD SEV) cipher DRAM contents and I/O, ensuring plaintext is visible only inside the enclave boundary (Sahita et al., 2023, Tseng et al., 2021).
• Sealed Storage: TEEs can persist data at rest by encrypting it with a sealing key, often derived from enclave measurement and hardware secrets (Sahita et al., 2023).
• Threat Model: The adversary controls all platform software—OS, hypervisor, drivers, cloud orchestration—and may perform physical attacks short of invasive decapsulation. Confidentiality and integrity hold unless hardware is physically compromised, or strong side-channels (timing, power, micro-arch leakage) break isolation (Sahita et al., 2023, Ozga et al., 2023).

2. Protocols and Cryptographic Mechanisms

Confidential computing protocols combine attestation, secure channels, and cryptographic partitioning. Canonical workflows include:

• Remote Attestation Workflows: For SGX, the enclave uses a hardware-signed attestation "quote" containing MRENCLAVE and signs it with a device key (Sarkar et al., 17 Jan 2024). The verifier checks code measurement before sending keys. For SEV-SNP, the Platform Security Processor emits a signed report binding VM image, firmware, and boot state (Shang et al., 5 Dec 2024).
• End-to-End Encryption: Data and code provisioned to enclaves are encrypted—commonly via public-key cryptography (Enc_{pk_{TEE}}(m))—until inside the enclave, with only ephemeral ID exposure (Sturzenegger et al., 2020).
• Sealing and Key Derivation: Sealing keys and enclave secrets are derived as

$$K_{seal} = KDF(K_0, "seal" \parallel \mu \parallel TID)$$

where $K_0$ is the hardware root, $\mu$ is the enclave measurement, and $TID$ is a unique identifier (Sahita et al., 2023).

• Data Partitioning and Minimization: No global database is kept; only ephemeral/minimized identifiers and necessary computation results persist (e.g., pairwise notification only, as in privacy-preserving contact tracing (Sturzenegger et al., 2020)).
• Composite Attestation: Multiple roots-of-trust (e.g., TEE and TPM) may collaborate to produce composite JWT-style tokens and merge static and dynamic measurement chains (Shang et al., 5 Dec 2024).

3. System Designs and Realizations

System implementations vary by platform and workload:

• Cloud and HPC Workloads: Confidential HPC relies on TEEs to isolate HPC jobs inside enclaves: SGX enclaves for containerized binaries, or SEV-encrypted VMs for full-OS isolation. Optimizations include oblivious I/O via ORAM, application-specific oblivious primitives (e.g., CMOV), trusted MPI libraries for enclave-to-enclave communication, and SGX-based MapReduce frameworks (SGX-MR) (Chen, 2022).
• Edge-to-Cloud Hierarchy: Hierarchical deployments split sensitive data and computation between edge TEEs (ARM TrustZone, HSM) and central cloud TEEs (Intel SGX, AMD SEV), using techniques like key splitting/quasigroup secret sharing and permissioned blockchains to manage identity and lineage (Alaverdyan et al., 2023, Zobaed et al., 2023).
• Processing-In-Memory (PIM): New architectures such as PIM-Enclave move confidential compute inside memory banks, using AES-GCM DMA for memory-bus encryption, remote attestation, and host exclusion during secure computation (Duy et al., 2021).
• Embedded and VM-Based TEEs: Open RISC-V architectures (e.g., CoVE, ACE) minimize the TCB by extending confidential qualifiers at the ISA level, using strictly partitioned memory regions enforced by MTT or PMP, and formally verifying isolation properties in Rust/Coq (Sahita et al., 2023, Ozga et al., 19 May 2025, Ozga et al., 2023).
• GPU and NPU—AI Acceleration: Recent industry solutions (NVIDIA Hopper, Ascend-CC) extend the TEE paradigm to GPU/TPU/NPU platforms by exclusively entrusting device hardware, with attested firmware, memory-bus encryption (AES-CTR, AES-GCM), delegated memory semantics, and cryptographic integrity checks on operator binaries and inference workflows (Dhar et al., 16 Jul 2024, Gu et al., 3 Jul 2025).
• Transparency Frameworks: In recognition that attestation alone cannot eliminate trust dependencies or backdoors, multi-level transparency frameworks—first-party endorsements, reproducible third-party builds, and full open-source with public logs—are emerging to build auditable chains of trust (Kocaoğullar et al., 5 Sep 2024).

4. Security Goals, Guarantees, and Limitations

Confidential computing aims for:

• Confidentiality: Adversaries outside the enclave cannot learn any secret inputs, ephemeral IDs, or processed data. Formally, for any pair $x_0,x_1$ indistinguishable to $A$, $$\Pr[A~\text{distinguishes}~Enc_{k_{HW}}(x_0), Enc_{k_{HW}}(x_1)] \leq \text{negl}(\lambda)$$ (Chen, 2022).
• Integrity: Loaded code is measured and locked; any tampering produces detectable faults or MAC failures.
• Minimal Leakage: Side-channels (timing, page-faults, cache) may leak up to $O(1)$ bits per observable per step; mitigation via oblivious operations or ORAM adds overhead but bounds leakage (Chen, 2022).
• Attested Computation: Composite attestation (involving both TEE and TPM) provides unified proofs, with protocol correctness and token integrity shown under Protocol Composition Logic (Shang et al., 5 Dec 2024).
• Data Minimization: Infected users store only ephemeral IDs, with database size and query patterns concealable via padding or dummy queries (Sturzenegger et al., 2020).
• Broader Impact: TEEs can eliminate the need for trusted third parties, enforce verifiable execution, and facilitate cross-cloud chains of trust for multi-party computation and confidential AI inference (Shang et al., 5 Dec 2024).

Known limitations include:

• Resource Constraints: DRAM and enclave memory size limit large models—partitioning and tiling may be needed, adding latency (Mo et al., 2022, Chen, 2022).
• Performance Overhead: Overhead ranges from 5–30% for memory encryption and attestation; per-tuple match (O(n)), setup costs (~200 ms) and throughput drops (~20–30%) for SGX-based deployments (Sturzenegger et al., 2020).
• Side-Channel Risks: Hardware side-channels remain incompletely addressed. No end-to-end proof of leakage bounds in distributed coordination or large-scale messaging (Chen, 2022, Zobaed et al., 2023).
• TCB Bloat: LibOS approaches can balloon the trusted code base, exposing new attack surfaces.
• Scale and Interoperability: Launching thousands of enclaves (e.g., for parallel HPC jobs) results in attestation bottlenecks; no standardized cross-architecture attestation protocol for multi-cloud (Chen, 2022, Shang et al., 5 Dec 2024).

5. Transparency, Trust, and Adoption Pathways

A significant barrier to widespread adoption is the gap between theoretical attestation and practical system trust:

• Transparency as Trust Amplifier: Attestation ensures loaded binaries match measurements, but not deep review. Transparency (endorsement logs, reproducible builds, open source) mitigates the need for naive trust in vendor binaries, making review/audit visible and verifiable (Kocaoğullar et al., 5 Sep 2024).
• Tiered Frameworks: Multi-level frameworks enable incremental improvement: L1 (first-party review and public logs), L2 (third-party certification with reproducible builds), L3 (community/open source and alerting). Comfort and willingness to share sensitive data increase monotonically with transparency level (Kocaoğullar et al., 5 Sep 2024).
• Metrics: Transparency score $T_i$ synthesizes configuration disclosures $(C_i)$, audits $(A_i)$, and documentation $(D_i)$ as

$$T_i = w_C C_i + w_A A_i + w_D D_i,\quad w_C + w_A + w_D = 1$$

Higher $T_i$ correlates with security and adoption.

• Empirical Impact: User studies confirm ~0.20 increase in sharing probability for each step up in transparency (None → L1 → L2 → L3), with high-detail briefings reducing misconceptions about certifier access and expertise (Kocaoğullar et al., 5 Sep 2024).
• Recommendations: Standardize log formats and processes; adopt open-source approaches where feasible; combine attestation with community auditing to create comprehensive chains of trust.

6. Prospective Research Directions and Challenges

Open research problems in confidential computing span:

• Formal Security Models: Unified theorems quantifying residual side-channel leakage in distributed/multi-party workflows.
• Composable Attestation: Protocols for cross-vendor, cross-cloud provenance, and migration that unify TPM/TEE proofs (Shang et al., 5 Dec 2024).
• Side-Channel-Hardened Primitives: Further development of constant-time, oblivious, and noise-injecting hardware primitives.
• Multi-party and Federated Workloads: Provenance logging and privacy-preserving coordination in collaborative workflows.
• Hardware Innovations: Extensions for PIM-based enclaves, confidential AI acceleration (NPU/GPU/FPGA), and domain-based memory partitioning (Duy et al., 2021, Dhar et al., 16 Jul 2024, Gu et al., 3 Jul 2025).
• Transparency Coupled to Attestation: Integration of reproducible builds, alerting, and certification in enclave and TCB measurement for robust, multi-tier verification (Kocaoğullar et al., 5 Sep 2024).

7. Practical Applications and Impact

Confidential computing’s applicability includes:

• Privacy-Preserving Contact Tracing: SGX enclaves process ephemeral contact IDs, match infection reports, and issue exposure notifications without global databases or re-identification risk (Sturzenegger et al., 2020).
• High-Performance Data Analytics: Confidential HPC jobs run in cloud enclaves, with O(1.3×) slowdown for enclaved MapReduce, and adaptive frameworks for MPI coordination (Chen, 2022).
• Cloud and Edge AI: Multi-tenant big data analytics and confidential Search (e.g., SAED, ClusPr algorithms) protect data-in-use in both edge and cloud, with hardware-assisted enclaves managing clustering, semantic ranking, and multi-tenant model serving (Zobaed, 2023, Zobaed et al., 2023).
• Healthcare, Financial Services: Deployments leverage tiered transparency to increase user trust for highly sensitive data categories (Kocaoğullar et al., 5 Sep 2024).
• Embedded and Edge Devices: Verified RISC-V ACE/CoVE schemes for mission-critical systems and multi-party collaborative tasks (Sahita et al., 2023, Ozga et al., 19 May 2025).

Confidential computing thus constitutes a multi-layered, hardware-rooted discipline for trusted in-use data and computation, combining TEEs, cryptographic protocols, attestation, software abstractions, transparency mechanisms, and ongoing formalization efforts across heterogeneous platforms. Its evolution is marked by a recurring tension among hardware resource constraints, performance, transparency, interoperability, and side-channel minimization, with active research addressing each dimension.