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phe snippet updated
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Sefik Ilkin Serengil 2025-03-05 12:41:42 +00:00 committed by GitHub
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@ -414,7 +414,7 @@ def on_prem():
onprem_cs.export_keys("secret.txt")
onprem_cs.export_keys("public.txt", public=True)
# find l2 normalized vector embeddings - VGG-Face already does
# find l2 normalized and all positive vector embeddings - VGG-Face already does
source_embedding = DeepFace.represent("img1.jpg")[0]["embedding"]
# encrypt source embedding
@ -422,10 +422,10 @@ def on_prem():
return encrypted_source_embedding
def cloud(encrypted_source_embedding):
# build the cryptosystem in cloud with only public key
# restore the built 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
# find l2 normalized and all positive vector embeddings - VGG-Face already does
target_embedding = DeepFace.represent("target.jpg")[0]["embedding"]
# find dot product of encrypted embedding and plain embedding
@ -434,16 +434,14 @@ def cloud(encrypted_source_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
def proof_of_work(encrypted_cosine_similarity, threshold = 0.68):
# build the cryptosystem on-prem with secret key
def verify(encrypted_cosine_similarity, threshold = 0.68):
# restore the built cryptosystem on-prem with secret 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]
print("same person" if cosine_similarity >= 1 - threshold else "different persons")
```