Privacy-Preserving Inference

This pragmatic technique involves splitting a single LLM into multiple components that are executed in different trust domains. For example, the initial, non-sensitive layers of the model could run on an untrusted client device, generating intermediate embeddings. These embeddings (which reveal less about the raw input) are then sent to a secure server (e.g., using a TEE) to complete the final, sensitive layers of computation.

• Key Mechanism: Architectural decomposition based on sensitivity and compute requirements.
• Primary Use Case: Optimizing the performance-privacy trade-off by minimizing the amount of data or computation that needs rigorous protection.
• Trade-off: Requires careful model analysis to identify optimal split points and may still leak some information via embeddings.

Enables analysis of patient medical records, imaging data, and genomic sequences without centralizing sensitive Protected Health Information (PHI). For example, a hospital can query a diagnostic model about a patient's MRI scan. Using homomorphic encryption, the encrypted scan is sent to the model, which returns an encrypted diagnosis, ensuring the cloud provider never sees the raw image or the result. This is critical for compliance with regulations like HIPAA and GDPR.

Allows banks to screen transactions and customer communications for fraud patterns without exposing raw financial data. A secure multi-party computation (MPC) protocol could allow multiple banks to collaboratively train and run a fraud detection model on their combined transaction data. No single bank ever sees another's raw customer data, but the collective model benefits from a broader view of fraud patterns, improving detection rates for all participants while maintaining strict data sovereignty.

Facilitates the automated review of confidential legal contracts, merger documents, and case files. A law firm can use a private inference service to identify clauses, assess risks, or perform due diligence. The model provider cannot learn the contents of the privileged documents or the specific legal strategies being analyzed. This application directly addresses attorney-client privilege and is essential for multi-document legal reasoning systems used in high-stakes corporate transactions.

Deploys internal chatbots for employees that can answer questions based on proprietary company data—such as product roadmaps, financial forecasts, or employee records—without that data leaving the corporate firewall or being exposed to the model vendor. Techniques like confidential computing (using secure enclaves) or federated inference allow the model to run within a trusted execution environment on-premises, ensuring intellectual property and trade secrets are never decrypted in an untrusted cloud.

Runs language model inference directly on a user's smartphone or laptop, ensuring personal data (messages, emails, location) never leaves the device. This is achieved through model compression (quantization, pruning) and tiny machine learning frameworks that create small, efficient models capable of local execution. For example, a voice assistant can process audio and generate responses entirely offline. This is the ultimate form of privacy-preserving inference, eliminating the data transmission risk entirely and enabling use in edge AI architectures.

Enables competitors or regulated entities in the same industry (e.g., pharmaceutical companies, telecom providers) to jointly analyze trends or benchmark performance without sharing business secrets. Using MPC or federated learning for inference, each party submits an encrypted query to a shared model. The aggregated, anonymized insights can be revealed, but the individual proprietary inputs remain confidential. This supports collaborative research and synthetic data generation for training while preserving competitive advantage.

A cryptographic technique that allows computations to be performed directly on encrypted data without needing to decrypt it first. For LLM inference, this means a user's encrypted query can be sent to a server, the model runs on the ciphertext, and an encrypted result is returned, which only the user can decrypt. Key properties include:

• Fully Homomorphic Encryption (FHE): Supports arbitrary computations but is computationally intensive.
• Partially Homomorphic Encryption: Supports only specific operations (e.g., addition or multiplication) but is more efficient.
• Enables secure outsourcing of computation to untrusted cloud environments.

A cryptographic protocol that enables multiple parties to jointly compute a function over their private inputs while keeping those inputs concealed from each other. In LLM inference, this can be used to split a model or data among several servers. Common frameworks and approaches include:

• Garbled Circuits: Enables secure evaluation of Boolean circuits.
• Secret Sharing: Data is split into shares distributed among parties; computation proceeds on the shares.
• Private Inference: A model owner and a data owner can collaboratively compute a prediction without either revealing their private asset (model weights or input data).

A decentralized machine learning paradigm where the model is trained across multiple edge devices or servers holding local data samples, without exchanging the raw data itself. While primarily a training technique, its principles are foundational for privacy. Key concepts:

• Local Training: Devices compute model updates on their private data.
• Secure Aggregation: Updates are cryptographically aggregated (e.g., using MPC or differential privacy) before being sent to a central server.
• Cross-silo vs. Cross-device: Differentiates between a few organizational servers vs. millions of mobile devices.

A rigorous mathematical framework that guarantees the output of a computation (e.g., a query on a dataset or a model's prediction) does not reveal whether any single individual's data was included in the input. It works by carefully adding calibrated statistical noise. Applied to inference:

• Local DP: Noise is added to the user's data before it is sent for inference.
• Global DP: Noise can be added to the model's outputs or internal activations.
• Provides a quantifiable, worst-case privacy guarantee against any adversarial analysis.

Secure, isolated areas within a main processor (e.g., Intel SGX, AMD SEV, ARM TrustZone) that protect code and data from the rest of the system, including the operating system and hypervisor. For privacy-preserving inference:

• The LLM and user query are loaded into the secure enclave.
• Computation occurs within this hardware-protected "black box."
• The host server cannot observe the plaintext data or model weights.
• Provides strong confidentiality and integrity guarantees based on hardware root of trust, with lower overhead than pure cryptographic methods.

The paradigm of running model inference directly on a user's local device (smartphone, IoT device, laptop) instead of sending data to a remote server. This is the ultimate form of data privacy, as raw data never leaves the device. Enabling technologies:

• Model Compression: Techniques like quantization, pruning, and knowledge distillation to shrink models for edge deployment.
• Small Language Models (SLMs): Efficient, domain-specific models designed for constrained hardware.
• Neural Processing Units (NPUs): Dedicated hardware accelerators in modern devices for efficient AI workloads.
• Eliminates network latency and enables operation without cloud connectivity.