How to Prepare Encryption for Confidential AI

Confidential AI refers to the ability to train and run machine learning models on encrypted data. This is critical for sectors like healthcare, finance, and enterprise analytics where data privacy is paramount. Traditional cloud-based AI requires sending raw data to a server, creating significant security and compliance risks. Confidential AI encryption solves this by allowing computations to be performed directly on ciphertext, the encrypted form of data, ensuring the raw information is never revealed to the processing environment, be it a cloud provider or a third-party service.

The core cryptographic primitive enabling this is Fully Homomorphic Encryption (FHE). FHE allows arbitrary computations (addition and multiplication) on encrypted data. When you apply an operation to ciphertexts, the result, once decrypted, matches the result of the same operation performed on the original plaintexts. For example, with FHE, a hospital could send encrypted patient records to a research cloud. The cloud could train a model to predict disease risk on the encrypted data and return an encrypted prediction, which only the hospital can decrypt. The cloud never sees the patient data or the final model weights.

Preparing data for Confidential AI involves a specific pipeline. First, data must be encoded into a format compatible with the FHE scheme, typically as integers within a specific range. Next, this encoded data is encrypted using the FHE scheme's public key. The choice of FHE library is crucial; popular open-source options include Microsoft SEAL, OpenFHE, and Concrete ML (from Zama). These libraries handle the complex underlying mathematics, providing APIs for encryption, computation, and decryption. The encrypted data is now ready to be sent to an untrusted environment for processing.

When writing code for FHE operations, you must consider its constraints. FHE computations are vastly slower and support limited precision compared to plaintext operations. A typical workflow in a library like Concrete ML involves: converting a standard model (e.g., a logistic regression) into an FHE-compatible circuit, quantizing the input data, and then compiling the circuit. The following is a simplified conceptual outline:

pythonCopy
# Pseudo-code structure using Concrete ML concepts
from concrete.ml.sklearn import LogisticRegression

# 1. Train a model on plaintext data (for simulation)
model = LogisticRegression()
model.fit(X_train_plain, y_train)

# 2. Compile the model to an FHE circuit
fhe_circuit = model.compile(X_train_plain)

# 3. Encrypt input and run inference in FHE
encrypted_prediction = fhe_circuit.encrypt_run_decrypt(X_test_encrypted)

The primary challenges in adopting Confidential AI are performance and developer experience. FHE computations can be 10,000 to 1,000,000 times slower than their plaintext equivalents and require significant memory. This makes them currently suitable primarily for inference on pre-trained models rather than full training. Furthermore, working with FHE requires deep cryptographic knowledge to manage noise growth, parameter selection, and circuit optimization. Emerging solutions like hybrid approaches combine FHE with other techniques like Secure Multi-Party Computation (MPC) or Trusted Execution Environments (TEEs) to balance performance and security for different parts of the ML pipeline.

To prepare for implementing Confidential AI, developers should start by exploring the documentation for the libraries mentioned. Key steps include: understanding the data type and precision limitations, profiling the performance of target models in a simulated FHE environment, and designing applications where the high latency of FHE is acceptable. The field is advancing rapidly, with new schemes like CKKS (for approximate arithmetic on real numbers) making encrypted deep learning more feasible. By encrypting data before it leaves its source, Confidential AI provides a powerful paradigm for privacy-preserving collaboration and analytics in Web3 and beyond.

The first prerequisite is selecting an appropriate encryption scheme. For confidential AI, you typically need homomorphic encryption (HE) or secure multi-party computation (MPC). HE, like the CKKS scheme for approximate arithmetic, allows computations on encrypted data without decryption. MPC, such as protocols from the MP-SPDZ library, enables multiple parties to jointly compute a function over their private inputs. The choice depends on your use case: HE is ideal for a single data owner outsourcing computation, while MPC suits collaborative scenarios between distrusting parties.

Next, you must establish a robust key management system. This is the most critical security component. For symmetric schemes, you need secure generation and storage of secret keys. For public-key systems like Paillier or BFV, you must manage key pairs and understand their lifecycle. In production, keys should be stored in a Hardware Security Module (HSM) or a managed service like AWS KMS or Hashicorp Vault. Never hardcode keys. Implement key rotation policies and use key encapsulation mechanisms (KEM) for secure distribution in distributed systems.

Your data must be prepared for encryption. This involves serialization and encoding. Numerical data (e.g., model weights, feature vectors) must be converted into a format the encryption library accepts, often large integers or polynomials. For the Microsoft SEAL library, you encode floating-point numbers into plaintext polynomials. Performance is paramount; encrypting high-dimensional data is expensive. Use dimensionality reduction (PCA) or quantization before encryption to reduce the plaintext space and speed up subsequent homomorphic operations.

Finally, integrate encryption into your existing ML pipeline. This requires modifying data loaders to encrypt inputs and adapting model architectures. For TensorFlow Encrypted or PySyft, you wrap tensors in encrypted protobufs. Benchmark the performance overhead, as encrypted inference can be 100-1000x slower. Plan for this by using model compression techniques and selecting efficient crypto parameters (e.g., polynomial degree, ciphertext modulus) that provide adequate security (e.g., 128-bit) without excessive computational cost. The pipeline must also handle the decryption and decoding of final results securely.

Confidential AI requires cryptographic techniques that allow computation on encrypted data. The primary goal is to ensure that raw input data, model parameters, and intermediate results are never exposed in plaintext to untrusted parties, such as cloud service providers. This is achieved through a combination of homomorphic encryption (HE), secure multi-party computation (MPC), and trusted execution environments (TEEs). Each approach offers a different trade-off between security guarantees, computational overhead, and implementation complexity, making the choice of preparation critical.

Homomorphic Encryption enables direct computation on ciphertexts. For AI, this means you can encrypt your training data, send it to a server, and the server can perform operations like matrix multiplications and activation functions without decrypting it. Libraries like Microsoft SEAL (for BFV/CKKS schemes) and OpenFHE provide the foundational tools. Preparation involves selecting an appropriate HE scheme—CKKS for approximate arithmetic on real numbers common in neural networks—and parameterizing it for the required security level and computational depth of your model.

Secure Multi-Party Computation (MPC) distributes the computation and secret data across multiple parties. No single party sees the complete dataset. For preparing a confidential AI pipeline, you must architect the system to split model weights and input data into secret shares. Frameworks like MP-SPDZ or industry solutions allow you to define the computation as a circuit or high-level program. The preparation phase involves setting up the communication channels, defining the participant roles (e.g., data providers, model owners), and implementing the specific MPC protocol (e.g., GMW, SPDZ).

Trusted Execution Environments (TEEs) like Intel SGX or AMD SEV provide hardware-isolated enclaves. Preparation here is less about cryptography and more about system security. You must partition your application into trusted and untrusted components, port critical AI inference code to run inside the enclave, and handle attestation to prove the enclave's integrity to remote clients. This involves using SDKs like the Intel SGX SDK and managing the complexities of limited enclave memory and secure data provisioning.

A practical preparation workflow involves several key steps. First, profile your model to understand its computational graph and arithmetic intensity. Second, select your primary cryptographic primitive (HE, MPC, TEE) based on your threat model and performance budget. Third, implement a proof-of-concept using a framework to benchmark latency and accuracy loss. Finally, design the key management and data pipeline, ensuring encryption keys are generated, stored, and rotated securely, often using a dedicated service like HashiCorp Vault or cloud KMS.

FHE enables computations on encrypted data without decryption, a core requirement for confidential AI. Unlike traditional encryption, which only protects data at rest or in transit, FHE protects data in use. This means a model provider can perform inference on your encrypted data without ever seeing the raw inputs or learning the results. For this to work, you must first encode your data into a format the FHE scheme can process and then encrypt it using a public key.

The most common approach uses the CKKS (Cheon-Kim-Kim-Song) scheme, which supports approximate arithmetic on real and complex numbers—ideal for machine learning. Before encryption, you must encode your floating-point data (e.g., a float32 tensor) into a plaintext polynomial. Libraries like Microsoft SEAL, OpenFHE, or Concrete ML handle this encoding. For a vector [0.5, -1.2, 3.1], the encoding process maps these values into the coefficients of a polynomial that the FHE circuit can understand.

Here is a simplified workflow using a Python-like pseudocode with Concrete ML, which automates much of the complexity:

pythonCopy
import numpy as np
from concrete.ml.torch.compile import compile_torch_model

# 1. Load your pre-trained PyTorch model
model = load_your_torch_model()

# 2. Compile the model for FHE inference
# This step quantizes the model and determines cryptographic parameters.
quantized_module = compile_torch_model(
    model,
    torch_inputset,  # Calibration data to set quantization ranges
    n_bits=8
)

# 3. Encrypt your input data for private prediction
x_encrypted = quantized_module.encrypt(x_clear)
# `x_encrypted` can now be sent to a server for private inference.

The compile_torch_model step is critical, as it quantizes the model to integer arithmetic and configures FHE parameters like polynomial degree and security level.

Key considerations during preparation include quantization and parameter selection. FHE operates on integers, so your model and data must be quantized (e.g., to 8-bit integers), which can affect accuracy. You must also choose cryptographic parameters—such as the polynomial modulus degree and ciphertext modulus—that balance security, performance, and computational capacity. Insufficient parameters break security; excessive parameters make computation impractically slow. Always refer to the latest library documentation, like the OpenFHE Security Guide, for recommended settings.

Finally, the encrypted input, typically one or more ciphertexts, is serialized into a transport format (often a byte array) and transmitted to the inference service. The entire preparation process must be performed client-side, with the secret key remaining exclusively with the data owner. This ensures that only the client can decrypt the final result, maintaining end-to-end confidentiality throughout the AI inference cycle.

ZK-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) enable a prover to convince a verifier that a statement is true without revealing any information beyond the statement's validity. In the context of confidential AI, this statement is the claim: "I correctly executed the training algorithm F on a private dataset D with private parameters P, resulting in a model with public hash H." The verifier receives only the public inputs—the model's commitment H and the hash of the training code—and a small cryptographic proof, gaining confidence in the training's integrity without accessing D or P.

The core technical challenge is representing the training computation as an arithmetic circuit or a Rank-1 Constraint System (R1CS), which is the format ZK-SNARK proving systems like Groth16, Plonk, or Halo2 require. This involves translating every step of the training loop—forward pass, loss calculation, backpropagation, and weight update—into a series of mathematical constraints over a finite field. For a neural network, each neuron activation and matrix multiplication becomes a set of addition and multiplication gates. Libraries like circom or arkworks are used to compile high-level training logic into this circuit representation.

Once the circuit is defined, the prover (the entity that performed the training) generates the proof. This process is computationally intensive, as it involves creating a witness (the private inputs that satisfy the circuit) and running the SNARK's proving algorithm. For example, using the Groth16 protocol, the prover would execute generate_proof(circuit, witness, proving_key). The output is a succinct proof, typically just a few hundred bytes, which can be verified near-instantly. The proving key and verification key are generated in a trusted setup ceremony specific to the training circuit.

Verification is the final and efficient step. Any party can verify the proof using the public verification key, the public output hash H of the trained model, and the proof itself: verify(verification_key, public_inputs, proof). A return value of true cryptographically guarantees that a model with hash H was produced by faithfully executing the attested training procedure. This enables use cases like submitting a verified model to a competition without leaking the training data, or proving to a decentralized oracle network that a model was trained on specific, legitimate data sources.

Optimizing these proofs for practical AI workloads is an active area of research. Key techniques include using zk-friendly neural architectures (replacing ReLU with polynomials), leveraging recursive proof composition to handle long training runs, and employing GPU/FPGA acceleration for proof generation. Projects like zkml and EZKL are building frameworks to streamline this process, allowing data scientists to export models from PyTorch and generate ZK proofs for their training or inference.

Multi-Party Computation (MPC) is a cryptographic protocol that allows multiple parties to jointly compute a function over their private inputs without revealing those inputs to each other. In federated learning, this means each client's local model updates can be aggregated into a final global model update without any single party—including the central server—learning the individual contributions. This is a stronger privacy guarantee than simple differential privacy, as it provides cryptographic security against curious or malicious aggregators. Common MPC frameworks for this task include MP-SPDZ and TF-Encrypted.

The core cryptographic primitive used is secret sharing. Instead of sending a plaintext model update (e.g., a gradient tensor), each client splits its update into multiple random shares. A single share reveals nothing about the original value. These shares are then distributed to different computation parties or servers. For a simple 2-party additive secret sharing scheme, a value x is split into two shares: x = x1 + x2. Client A sends x1 to Server 1 and x2 to Server 2. Neither server can reconstruct x alone.

The aggregation logic is then performed on the shares. The servers compute the sum of all received shares. Due to the linearity of the secret sharing scheme, summing the shares and then reconstructing the result is equivalent to reconstructing the shares and then summing the original values. After the secure aggregation is complete, the servers combine their final aggregated shares to reconstruct the global model update, which is then sent back to the clients. This process ensures the plaintext of any individual client's update is never exposed during transmission or computation.

Implementing this requires a coordination layer. A typical setup involves a coordinator server (which could be non-trusted) that orchestrates the training rounds and a set of computation servers (often two or more for security) that perform the MPC operations. The client workflow changes: instead of sending updates directly to the model aggregator, it secret-shares its update among the computation servers. Libraries like PySyft with PyGrid provide abstractions for this, handling the communication and cryptographic protocols between clients and servers.

Here is a simplified conceptual code snippet using a hypothetical MPC library, demonstrating the client's role in secret sharing a model gradient:

pythonCopy
import mpc_lib
import torch

# Client has computed a local gradient
gradient = model.get_gradients()

# Convert gradient to a fixed-point representation for crypto operations
gradient_fixed = mpc_lib.to_fixed_point(gradient)

# Split the gradient into secret shares for two servers
share_for_server_1, share_for_server_2 = mpc_lib.share(gradient_fixed, n_parties=2)

# Send each share to its respective computation server
send_to_server(SERVER_1_URL, share_for_server_1)
send_to_server(SERVER_2_URL, share_for_server_2)

The servers would then run a corresponding secure summation protocol on all received shares before reconstructing the result.

Key considerations for production include the performance overhead of cryptographic operations, which can be 10-100x slower than plaintext training, and the communication rounds required between servers. Using optimized libraries and selecting efficient MPC protocols (like Shamir's secret sharing for more than two parties or GMW for boolean circuits) is critical. The security model must also be defined: is the system secure against honest-but-curious (semi-honest) adversaries or malicious ones? Most practical FL implementations start with the honest-but-curious model due to its lower computational cost.

To prepare your project for confidential AI, start by conducting a threat model analysis. Define what data must remain private—is it the raw input data, the model weights, or the inference results? This decision dictates your primary cryptographic tool. For instance, protecting sensitive user queries against a public model favors FHE, while training a model on distributed private datasets is a classic use case for MPC. Tools like the OpenMined PySyft library provide a practical starting point for MPC simulations.

Next, architect your system with a hybrid approach. Pure on-chain FHE for complex models is currently impractical due to gas costs. A common pattern is to perform the heavy computation off-chain in a trusted execution environment (TEE) or a decentralized network, using the blockchain as a settlement and verification layer. For example, you could use zk-SNARKs to generate a proof that an inference was performed correctly within a secure enclave, then post the proof and encrypted result on-chain. Projects like zkML with EZKL demonstrate this verifiable inference pattern.

Finally, focus on iterative development and auditing. Begin with a minimal viable circuit or a small, non-sensitive dataset to benchmark performance and cost. Use development frameworks such as Zama's Concrete ML for FHE or Jigsaw's MPC libraries to prototype. Before mainnet deployment, engage specialists for formal verification of your cryptographic circuits and smart contracts. The goal is to move from a theoretical understanding to a deployed, audited system that balances confidentiality, computational integrity, and practical usability for the end-user.