> For the complete documentation index, see [llms.txt](https://platformdocs.lattica.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://platformdocs.lattica.ai/how-to-guides/secure-query-processing/how-to-encrypt-input-message.md).

# 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.

   <div data-gb-custom-block data-tag="hint" data-style="info" class="hint hint-info"><p>If the key pair is already defined, you do not need to create it again.</p></div>
2. **User Access Token**:
   * The user must have a valid **Access Token**, which provides permission to interact with the workload.
   * Tokens are unique to each user and workload.

***

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:

{% tabs %}
{% tab title="🧊 Python SDK" %}

```python
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)
```

{% endtab %}

{% tab title="📦TypeScript SDK" %}

```javascript
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);
```

{% endtab %}
{% endtabs %}

{% hint style="info" %}
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](/how-to-guides/secure-query-processing/how-to-encrypt-execute-and-decrypt-in-one-step.md)].
{% endhint %}