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phe snippet
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Sefik Ilkin Serengil 2025-03-08 20:20:58 +00:00 committed by GitHub
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@ -381,13 +381,12 @@ Even though vector embeddings are not reversible to original images, they still
```python ```python
from lightphe import LightPHE from lightphe import LightPHE
# define a plain vectors for source and target
alpha = DeepFace.represent("img1.jpg")[0]["embedding"] # user tower
beta = DeepFace.represent("target.jpg")[0]["embedding"] # item tower
# build an additively homomorphic cryptosystem (e.g. Paillier) on-prem # build an additively homomorphic cryptosystem (e.g. Paillier) on-prem
cs = LightPHE(algorithm_name = "Paillier", precision = 19) cs = LightPHE(algorithm_name = "Paillier", precision = 19)
# define a plain vector for source
alpha = DeepFace.represent("img1.jpg")[0]["embedding"] # user tower
# export keys # export keys
cs.export_keys("secret.txt"); cs.export_keys("public.txt", public=True) cs.export_keys("secret.txt"); cs.export_keys("public.txt", public=True)
@ -400,6 +399,9 @@ del cs, alpha
# restore the cryptosystem in cloud with only public key # restore the cryptosystem in cloud with only public key
cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt") cloud_cs = LightPHE(algorithm_name = "Paillier", precision = 19, key_file = "public.txt")
# define a plain vector for target
beta = DeepFace.represent("target.jpg")[0]["embedding"] # item tower
# dot product of encrypted and plain embedding pair # dot product of encrypted and plain embedding pair
encrypted_cosine_similarity = encrypted_alpha @ beta encrypted_cosine_similarity = encrypted_alpha @ beta