How-To: Encrypt Input Message

Prerequisites

1. Encryption Keys:

   • A Secret Key must be defined in the Query Client to encrypt and decrypt messages.

   • The corresponding Evaluation Key must be deployed in LatticaAI.

   If the key pair is already defined, you do not need to create it again.

2. User Access Token:

   • The user must have a valid Access Token, which provides permission to interact with the AI model.

   • Tokens are unique to each user and model.

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Use the following code snippet to encrypt the input message. The encryption process takes the User Access Token and the message to be encrypted as parameters:

🧊 Python SDK

import lattica_common.app_api as agent_app
​
# Notice your query token expires in 30 days
query_token = "the_query_token_you_got_using_the_generate_user_token"

# user_data is a tuple of: 
# (serialized_context, serialized_secret_key, serialized_homseq)
# which you need for encrypting the query and querying the model
user_data = agent_app.user.query_offline_phase(query_token)

dataset = pd.read_csv('data/mnist_data.csv').values / 255
data = torch.tensor(dataset[0])
serialized_ct = agent_app.user.encrypt(user_data, dataset)

📦TypeScript SDK

import { LatticaQueryClient } from 'lattica-query-client';

const client = new LatticaQueryClient('your-jwt-query-token');

// Uploaded the EK
const initialized = await client.init();
if (!initialized) {
  console.error('Initialization failed: The EK was not successfully uploaded.');
  throw new Error('EK upload failed.');
}
console.log('EK uploaded successfully.');

// The data you want to encrypt
const inputTensor = ....;

// Encrypt the provided input
const ct = await client.encrypt(pt);

This step focuses on a specific part of the query process: Input message encryption. If you prefer to perform encryption, query execution, and decryption in a single command, refer to [How-To: Encrypt, Execute, and Decrypt in One Step].