趋势解读：Private analytics via zero-trust aggregation，聚焦形式化数学证明能力

Adrià Gascón, Staff Research Scientist, Google Research, and Mariana Raykova, Senior Staff Research Scientist, Google We introduce a private analytics solution which leverages a new cryptographic protocol for secure aggregation combined with the transparency properties of TEEs to achieve state-of-the-art privacy and security guarantees. By processing data locally, on-device AI can provide enhanced protection and timely alerts while keeping user information private. For example, Android uses a system called SafetyCore to provide privacy-preserving on-device features and common infrastructure to protect users from unwanted content. When developing on-device technologies, teams need to understand how well their systems work across millions of individual smartphones, each with unique data distributions, varying hardware constraints, and different user behaviors. To achieve this in a way that reveals only collective trends without revealing individual user data, teams can leverage cryptographic secure aggregation as a key building block. Like all cryptographic protocols, secure aggregation uses advanced mathematical tools to provide its security assurance. Today, we set a higher bar for efficient cryptographic aggregation in a private analytics service. We follow a zero-trust principle, which aims to reduce trust necessary in any single entity. We achieve this through a new security design that combines cryptographic and hardware protection mechanisms. Our solution leverages a new cryptographic aggregation method that provably guarantees only anonymized, aggregated insights about a population can be obtained by Google. Additionally, trusted execution environments (TEEs) are used to provide a strict layer of attestation and transparency. When models are deployed locally on-device, simply knowing that a model is 'running' isn't enough to understand its behavior, effectiveness, or failure modes. This limits the ability to answer critical questions like: Is the model drifting? (e.g., Does a translation model struggle with new slang emerging in a specific region?) Are there hidden biases? (e.g., Is an image classifier less accurate under specific lighting conditions common in certain geographic areas?) What is the real-world error rate? (e.g., Is a "Smart Reply" feature being ignored because its suggestions are technically correct but socially awkward?) This is where private analytics becomes the essential bridge, enabling anonymized, aggregated insights about a population without ever revealing individual user content. Google teams use federated analytics for this kind of aggregated, private insight, with applications in Pixel Recorder , Gboard , and more. Federated analytics requires a private aggregation route, where the data from individual devices is protected until combined into a sum. Two paradigms have emerged to protect user data in this setting: hardware-based isolation (TEEs) and cryptographic protocols. The hardware approach centers on TEEs, such as Intel TDX , AMD SEV-SNP and others. The core idea is to create a "secure enclave" — essentially a protected slice of the processor and memory that is isolated from the rest of the device. Inside this enclave, data can be decrypted and processed in plaintext, shielded even from a compromised operating system or a malicious hypervisor. Through a process called attestation, TEEs can compute a hardware-backed cryptographic "fingerprint" of the exact firmware and software state running inside the enclave. For a user or an auditor, attestation offers a verifiable guarantee that the data is being handled by the specific, tamper-proof program they expect, rather than a modified version designed to leak information. Google has deployed TEE-backed differentially private aggregation for computing insights into AI systems in the Pixel Recorder app.