show each step of optimization in separate script

2_precompute.py

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+encoding_width = 2**13 # restricted by ckks size
+database_size = 100
+import time
+
+# Client Setup
+
+import numpy as np
+from Pyfhel import Pyfhel, PyCtxt
+
+print(f"[Client] Initializing Pyfhel session and data...")
+HE_client = Pyfhel()           # Creating empty Pyfhel object
+ckks_params = {
+    'scheme': 'CKKS',   # can also be 'ckks'
+    'n': 2**14,         # Polynomial modulus degree. For CKKS, n/2 values can be
+                        #  encoded in a single ciphertext. 
+                        #  Typ. 2^D for D in [10, 15]
+    'scale': 2**30,     # All the encodings will use it for float->fixed point
+                        #  conversion: x_fix = round(x_float * scale)
+                        #  You can use this as default scale or use a different
+                        #  scale on each operation (set in HE.encryptFrac)
+    'qi_sizes': [60, 30, 30, 30, 60] # Number of bits of each prime in the chain. 
+                        # Intermediate values should be  close to log2(scale)
+                        # for each operation, to have small rounding errors.
+}
+HE_client.contextGen(**ckks_params)  # Generate context for bfv scheme
+HE_client.keyGen() # Generates both a public and a private key
+HE_client.relinKeyGen()
+HE_client.rotateKeyGen()
+
+# Generate and encrypt query vector
+x = np.random.rand(encoding_width)
+# precompute 1 - |x|^2 for query vector
+du = 1 - x @ x
+cx = HE_client.encrypt(x)
+
+# Serializing data and public context information
+
+start = time.time()
+s_context    = HE_client.to_bytes_context()
+s_public_key = HE_client.to_bytes_public_key()
+s_relin_key  = HE_client.to_bytes_relin_key()
+s_rotate_key = HE_client.to_bytes_rotate_key()
+#s_cx         = [cx[j].to_bytes() for j in range(len(cx))]
+s_cx         = cx.to_bytes()
+end = time.time()
+
+print(f"[Client] Sending HE_client={HE_client} and cx={cx}")
+print(f"[Client] Sent {(len(s_context) + len(s_public_key) + len(s_relin_key) + len(s_rotate_key) + len(s_cx)) / (10**6)} MB")
+print(f"[Client] Setup took {end - start}s")
+print("="*40)
+
+# Server Mock
+
+def hyperbolic_distance_parts(u, v): # returns only the numerator and denominator of the hyperbolic distance formula
+    diff = u - v
+    return diff @ diff# returns the numerator and denominator
+
+# document matrix containing rows of document encoding vectors
+D = np.random.rand(database_size, encoding_width)
+# precompute 1 - |D|^2 for each row vector in D
+dv = []
+for i in range(len(D)):
+    v = D[i]
+    dv.append(1 - v @ v)
+dv = np.array(dv)
+
+HE_server = Pyfhel()
+HE_server.from_bytes_context(s_context)
+HE_server.from_bytes_public_key(s_public_key)
+HE_server.from_bytes_relin_key(s_relin_key)
+HE_server.from_bytes_rotate_key(s_rotate_key)
+cx = PyCtxt(pyfhel=HE_server, bytestring=s_cx)
+print(f"[Server] Received HE_server={HE_server} and cx={cx}")
+
+# Encode each document weights in plaintext
+res = []
+start = time.time()
+for i in range(len(D)):
+    cd = HE_server.encrypt(D[i])
+    res.append(hyperbolic_distance_parts(cx, cd))
+end = time.time()
+
+s_res = [res[j].to_bytes()for j in range(len(res))]
+
+print(f"[Server] Distances computed! Responding: res={res[0]}...")
+print(f"[Server] Sent {(np.sum([len(s_res[i]) for i in range(len(s_res))])) / (10**6)} MB")
+
+print(f"[Server] Compute took {end - start}s with bandwidth {len(D) / (end-start)} documents/s")
+print("="*40)
+
+# Note that the time is mostly restricted by database size and not encoding size
+
+# Client Parse Response
+
+def hyperbolic_distance(u, v):
+    num = ((u - v) @ (u - v))
+    den = (1 - (u @ u)) * (1 - (v @ v))
+    return np.arccosh(1 + 2 * (num / den))
+
+start = time.time()
+c_res = []
+for i in range(len(s_res)):
+    c_num = PyCtxt(pyfhel=HE_server, bytestring=s_res[i])
+    p_num = HE_client.decrypt(c_num)[0]
+    dist = np.arccosh(1 + 2 * (p_num / (du * dv[i])))
+    c_res.append(dist)
+end = time.time()
+
+print(f"[Server] Compute took {end - start}s with bandwidth {len(s_res) / (end-start)} documents/s")
+print("="*40)
+
+# Checking result
+expected_res = [hyperbolic_distance(x, np.array(w)) for w in D]
+for i in range(len(c_res)):
+    result = c_res[i]
+    expected = expected_res[i]
+    if np.abs(result - expected) < 1e-3:
+        pass
+    else:
+        print(f"got: {result}, expected: {expected}")
+        assert False