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Google has outlined a persistent server-side memory design for Private AI Compute that would let Gemini-based assistants retain user context across sessions and devices. Assistance-related data would reside in dedicated encrypted cloud storage, while the decryption keys remain exclusively on a user’s personal devices; the proposed capability is not yet generally available.
For requests requiring saved context, a user device would create an authenticated end-to-end encrypted connection to an isolated hardware-enforced secure enclave. The enclave would decrypt the data only in volatile memory, process the request, then re-encrypt updated context immediately. Google said device-derived per-user keys, software attestation, a public record of server software, and independent audits are intended to make the system verifiable and prevent Google or other parties from accessing users’ stored memory.
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Google announced a planned server-side persistent-memory capability for Private AI Compute, designed to let Gemini-based assistants retain encrypted context across sessions and devices. The proposed architecture keeps decryption keys on users' devices and uses authenticated end-to-end encrypted connections to secure cloud enclaves for temporary in-memory processing.
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Google has introduced Private AI Compute, a privacy-enhancing technology designed to process AI queries securely in the cloud while maintaining on-device-level privacy guarantees. The system leverages Trillium TPUs and Titanium Intelligence Enclaves, utilizing AMD-based Trusted Execution Environments (TEEs) to encrypt and isolate memory, ensuring that user data remains inaccessible to Google and protected from physical exfiltration attacks. The infrastructure supports mutual attestation and encrypted communication between trusted nodes, with strict controls to prevent unauthorized access and ensure that only validated workloads are executed. To assess the security of this new platform, Google engaged NCC Group for a comprehensive review, including architectural analysis and detailed cryptographic assessments of key components such as the Oak Session Library, attestation mechanisms, and the T-Log system. The review also covered configuration and source code analysis to evaluate the robustness of the system's privacy and security controls. This independent evaluation highlights Google's efforts to provide strong privacy assurances for cloud-based AI processing, addressing concerns about data confidentiality and integrity in modern cloud environments.
Jun 29, 2026
Apple said some **Apple Intelligence** requests that exceed on-device limits will now run in Google Cloud under an expanded **Private Cloud Compute (PCC)** architecture, while simpler tasks continue to be processed locally and other cloud workloads remain on Apple-operated servers. The company said its newer model stack includes on-device `AFM 3 Core` and `AFM 3 Core Advanced`, Apple-hosted `AFM 3 Cloud` and `ADM 3 Cloud`, and `AFM 3 Cloud Pro`, which runs on Google-owned NVIDIA hardware for more demanding queries. Apple said the privacy model is unchanged despite the move to third-party infrastructure, with protections built around stateless processing, non-targetability, no privileged runtime access, and verifiable transparency. To enforce those guarantees on Google Cloud, Apple said it is using **NVIDIA Confidential Computing**, **Intel TDX**, Google’s **Titan** security chip, Apple-signed software attestation, and a cryptographically verifiable append-only ledger of approved hardware; the company also plans to publish PCC binaries and extend researcher access through the **Apple Security Bounty** program.
Jun 29, 2026
Google DeepMind has piloted a double-blind evaluation framework for its proprietary **Gemini 2.5 Flash Lite** model, designed to prevent benchmark leakage and contamination. External evaluators can submit confidential prompts, benchmarks, and scoring code without exposing them to Google, while Google keeps model weights and inference code confidential; the pilot is intended to validate the testing method rather than publish model-performance scores. The project involves the Singapore AI Safety Institute, OpenMined, AVERI, and MLCommons, using private benchmarks within a cryptographically protected environment. Its architecture combines Google Cloud Confidential Space, an NVIDIA H100 Confidential GPU, Intel TDX-encrypted host memory, remote attestation, and OpenMined PySyft controls. The safeguards go beyond contractual confidentiality and zero-logging practices, but the model still requires trust in Google-operated attestation and signing systems, the cloud platform, and underlying hardware vendors.
Aug 27, 2026