> For the complete documentation index, see [llms.txt](https://docs.envector.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.envector.io/1.4.x/sdk-user-guide/advanced-user-guide/cipher/decryption.md).

# Decryption

Decryption is the process of converting an encrypted `CipherBlock` object or an encrypted score back into its original, human-readable plaintext form. The `Cipher` class provides different methods and configurations depending on your specific needs.

***

## 1. Standard Decryption (`decrypt`)

This is the most common decryption operation. It is used to decrypt a `CipherBlock` that contains a full data vector. You'll need the secret key that corresponds to the encryption key used to create the data.

The `cipher.decrypt()` method takes the encrypted object and the path to the secret key.

**Example**

```python
from pyenvector import Cipher

# Assume 'encrypted_item' is a CipherBlock object you have retrieved
# encrypted_item = db.get_item(...)

# Initialize the Cipher class
cipher = Cipher(dim=512)

encrypted_item = cipher.encrypt([0.0] * 512, "item", "keys/example/EncKey.json")
# Decrypt the object by providing the secret key path
decrypted_vector = cipher.decrypt(
    encrypted_item, 
    sec_key_path="keys/example/SecKey.json"
)

print("Decrypted vector:", decrypted_vector)
# Output: Decrypted vector: [0.0, 0.0, 0.0, ...]
```

***

## 2. Decryption with a Sealed Secret Key

For enhanced security, you can use a "sealed" secret key. A sealed key is a secret key that has been encrypted with another key, known as a Key Encryption Key (KEK). This prevents the secret key from ever being exposed in plaintext on your system.

To use this feature, you must initialize the `Cipher` class with the `seal_mode` and `seal_key_path`. The actual decryption call remains the same, but the setup is more secure.

**Example**

```python
from pyenvector import Cipher

# 1. Initialize Cipher with the sealing configuration
# This tells the class how to "unseal" your secret key
cipher = Cipher(
    dim=512,
)
encrypted_item = cipher.encrypt([0.0] * 512, "item", "keys/example-sealed/EncKey.json")
# 2. Decrypt as usual, but provide the path to the SEALED secret key
# The Cipher class will handle unsealing it in memory before use
decrypted_vector = cipher.decrypt(
    encrypted_item,
    sec_key_path="keys/example-sealed/SecKey.json",
    seal_mode="aes",
    seal_kek_path="aes.kek",
)

print("Decrypted vector (from sealed key):", decrypted_vector)
```

***

## **3. Decrypting Scores (decrypt\_score)**

\
When you call `index.scoring()`, the result is not a single value but an encrypted vector of similarity scores (e.g., inner products with all stored entries in the index).\
Since each score remains encrypted, you need to use the `Cipher.decrypt_score()` method to reveal the plaintext values.

**Basic Usage**

```python
from pyenvector import Cipher

# Assume 'encrypted_scores' is the result from index scoring
# encrypted_scores = index.scoring(encrypted_query)

# Initialize the Cipher class
cipher = Cipher(dim=512)

# Decrypt the score vector to get the plaintext similarity scores
plaintext_scores = cipher.decrypt_score(
    encrypted_scores,
    sec_key_path="keys/example/SecKey.json"
)

print(f"The plaintext similarity scores are: {plaintext_scores} ✅")
# Output: The plaintext similarity scores are: [0.8765, 0.6543, 0.4321, ...] ✅

```

#### Using a Sealed Secret Key

If you are working with a sealed secret key (`SecKey.json`), you must also provide the `seal_kek_path` option.

```python
from pyenvector import Cipher

# Assume 'encrypted_scores' is the result from index scoring
# encrypted_scores = index.scoring(encrypted_query)

# Initialize the Cipher class
cipher = Cipher(dim=512)

# Decrypt the score vector with the sealed secret key
plaintext_scores = cipher.decrypt_score(
    encrypted_scores,
    sec_key_path="keys/example-sealed/SecKey.json",
    seal_mode="aes",
    seal_kek_path="aes.kek",
)

print(f"The plaintext similarity scores are: {plaintext_scores} ✅")
# Output: The plaintext similarity scores are: [0.8765, 0.6543, 0.4321, ...] ✅

```


---

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