Private analytics via zero-trust aggregation (opens in new tab)
Google presents a private analytics architecture combining one-shot cryptographic secure aggregation with trusted execution environments (TEEs). The design follows a zero-trust model: cryptography prevents individual data from being reconstructed, while TEE attestation verifies that approved, publicly auditable code is running. It aims to provide useful population-level insights without exposing raw user data, even if hardware protections are compromised. ## Why Private Analytics Matters - On-device AI protects user information, but developers still need to measure performance across millions of devices. - Aggregated analytics can reveal: - Model drift, such as difficulty handling new language or slang. - Hidden biases across regions, environments, or demographic conditions. - Real-world error rates and user reactions to model outputs. - Google already uses federated analytics in products including Pixel Recorder and Gboard. ## Limitations of Existing Protections - **TEEs** isolate sensitive computation from compromised operating systems and hypervisors. - Hardware attestation produces a cryptographic fingerprint proving which firmware and software are running. - However, TEEs remain vulnerable to newly discovered side-channel attacks. - Traditional cryptographic aggregation offers mathematical privacy guarantees, but many protocols require devices to stay online through several interactive rounds. - Extended multiround participation limits practical deployment at large scale. ## Combining Encryption and Isolation - The new system allows devices to submit data in a single message. - This removes the need for devices to remain connected for multiple protocol rounds. - Data is encrypted before leaving the device and is never reconstructed in server memory, including inside a TEE. - Unencrypted information is processed off-device only after it has been aggregated and anonymized. - TEE attestation provides verifiable evidence that the intended secure aggregation implementation is running correctly. - The cryptographic and hardware layers provide defense in depth: failure of one layer does not automatically expose individual data. ## One-Shot Lattice-Based Aggregation - The protocol uses lattice-based cryptography. - Ciphertexts can be combined so that their underlying data—and encryption keys—are aggregated together. - A resulting decryption key can reveal only the aggregate, not individual contributions. - Small client committees hold decryption hints and help unlock the aggregated result. - Differential privacy noise is added to further protect the aggregate. - Decryption authority is distributed across multiple parties, preventing any single party from accessing encrypted data. ## Application to Android SafetyCore - SafetyCore provides privacy-preserving, on-device safety features for Android 9 and later. - Private analytics can help measure which threats are detected and identify areas where detection needs improvement. - Google is working with the Android SafetyCore team to apply this system while preserving the confidentiality of individual users’ data. ## Practical Takeaway The approach pairs the scalability of one-shot cryptographic aggregation with the verifiability of TEEs. It is designed for private, large-scale analytics where neither individual data nor trust in a single hardware or software component is required.