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emphasize on prem and cloud
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@ -401,40 +401,57 @@ Conversely, if your task involves facial recognition on small to moderate-sized
**Encrypt Embeddings** - `Demo with PHE`, [`Demo with FHE`](https://youtu.be/njjw0PEhH00), [`Tutorial for PHE`](https://sefiks.com/2025/03/04/vector-similarity-search-with-partially-homomorphic-encryption-in-python/), [`Tutorial for FHE`](https://sefiks.com/2021/12/01/homomorphic-facial-recognition-with-tenseal/)
Even though vector embeddings are not reversible to original images, they still contain sensitive information such as fingerprints, making their security critical. Encrypting embeddings is essential for higher security applications to prevent adversarial attacks that could manipulate or extract sensitive information. Traditional encryption methods like AES are very safe but limited in securely utilizing cloud computational power for distance calculations. Herein, [homomorphic encryption](https://youtu.be/3ejI0zNPMEQ), allowing calculations on encrypted data, offers a robust alternative. In summary, we are able to compute similarity between encrypted embeddings with homomorphic encryption.
Even though vector embeddings are not reversible to original images, they still contain sensitive information such as fingerprints, making their security critical. Encrypting embeddings is essential for higher security applications to prevent adversarial attacks that could manipulate or extract sensitive information. Traditional encryption methods like AES are very safe but limited in securely utilizing cloud computational power for distance calculations. Herein, [homomorphic encryption](https://youtu.be/3ejI0zNPMEQ), allowing calculations on encrypted data, offers a robust alternative. In summary, we are able to compute encrypted similarity between encrypted embeddings with homomorphic encryption.
```python
from lightphe import LightPHE
# build an additively homomorhic encryption cryptosystem
onprem_cs = LightPHE(algorithm_name = "Paillier", precision = 19)
def on_prem():
# build an additively homomorhic encryption cryptosystem
onprem_cs = LightPHE(algorithm_name = "Paillier", precision = 19)
# export keys
onprem_cs.export_keys("secret.txt")
onprem_cs.export_keys("public.txt", public=True)
# find l2 normalized vector embeddings - VGG-Face already does
source_embedding = DeepFace.represent("img1.jpg")[0]["embedding"]
# encrypt source embedding
encrypted_source_embedding = onprem_cs.encrypt(source_embedding)
# export public key
onprem_cs.export_keys("public.txt", public=True)
return encrypted_source_embedding
# build cryptosystem in cloud with only public key
cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt")
def cloud(encrypted_source_embedding):
# build the cryptosystem in cloud with only public key
cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt")
# find l2 normalized vector embeddings - VGG-Face already does
target_embedding = DeepFace.represent("target.jpg")[0]["embedding"]
# find dot product of encrypted embedding and plain embedding in cloud
encrypted_cosine_similarity = encrypted_source_embedding @ target_embedding
# find l2 normalized vector embeddings - VGG-Face already does
source_embedding = DeepFace.represent("img1.jpg")[0]["embedding"]
target_embedding = DeepFace.represent("target.jpg")[0]["embedding"]
# confirm that cloud cannot decrypt it even though it is calculated by cloud
with pytest.raises(ValueError, match="must have private key"):
cloud_cs.decrypt(encrypted_cosine_similarity)
return encrypted_cosine_similarity
# encrypt source embedding on-prem
encrypted_source_embedding = onprem_cs.encrypt(source_embedding)
# find dot product of encrypted embedding and plain embedding in cloud
encrypted_cosine_similarity = encrypted_source_embedding @ target_embedding
# confirm that cloud cannot decrypt it even though it is calculated by cloud
with pytest.raises(ValueError, match="You must have private key"):
cloud_cs.decrypt(encrypted_source_embedding)
# restore cosine similarity on prem
cosine_similarity = onprem_cs.decrypt(encrypted_cosine_similarity)[0]
# proof of work
expected_similarity = sum(x * y for x, y in zip(source_embedding, target_embedding))
assert abs(cosine_similarity - expected_similarity) < 1e-2
def proof_of_work(encrypted_cosine_similarity):
# build the cryptosystem in cloud with private key
cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "secret.txt")
# restore cosine similarity
cosine_similarity = onprem_cs.decrypt(encrypted_cosine_similarity)[0]
# distance threshold for VGG-Face and cosine
threshold = 0.68
if cosine_similarity >= 1 - threshold:
print("same person")
else:
print("different persons")
```
Check out [`LightPHE`](https://github.com/serengil/LightPHE) library to find out more about partially homomorphic encryption.