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advanced-real-numbers-128.py
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advanced-real-numbers-128.py
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from openfhe import *
import time # to enable TIC-TOC timing measurements
def automatic_rescale_demo(scal_tech):
if(scal_tech == ScalingTechnique.FLEXIBLEAUTO):
print("\n\n\n ===== FlexibleAutoDemo =============\n")
else:
print("\n\n\n ===== FixedAutoDemo =============\n")
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(6)
parameters.SetScalingModSize(90)
parameters.SetScalingTechnique(scal_tech)
parameters.SetBatchSize(batch_size)
cc = GenCryptoContext(parameters)
print(f"CKKS scheme is using ring dimension {cc.GetRingDimension()}\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalMultKeyGen(keys.secretKey)
# Input
x = [1.0, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
# Computing f(x) = x^18 + x^9 + d
#
# In the following we compute f(x) with a computation
# that has a multiplicative depth of 5.
#
# The result is correct, even though there is no call to
# the Rescale() operation.
c2 = cc.EvalMult(c, c) # x^2
c4 = cc.EvalMult(c2, c2) # x^4
c8 = cc.EvalMult(c4, c4) # x^8
c16 = cc.EvalMult(c8, c8) # x^16
c9 = cc.EvalMult(c8, c) # x^9
c18 = cc.EvalMult(c16, c2) # x^18
c_res1 = cc.EvalAdd(cc.EvalAdd(c18, c9), 1.0) # Final result 1
c_res2 = cc.EvalSub(cc.EvalAdd(c18,c9), 1.0) # Final result 2
c_res3 = cc.EvalMult(cc.EvalAdd(c18,c9), 0.5) # Final result 3
result1 = cc.Decrypt(c_res1,keys.secretKey)
result.SetLength(batch_size)
print("x^18 + x^9 + 1 = ", result1)
result2 = cc.Decrypt(c_res2,keys.secretKey)
result.SetLength(batch_size)
print("x^18 + x^9 - 1 = ", result2)
result3 = cc.Decrypt(c_res3,keys.secretKey)
result.SetLength(batch_size)
print("0.5 * (x^18 + x^9) = ", result3)
def manual_rescale_demo(scal_tech):
print("\n\n\n ===== FixedManualDemo =============\n")
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(5)
parameters.SetScalingModSize(90)
parameters.SetBatchSize(batch_size)
cc = GenCryptoContext(parameters)
print(f"CKKS scheme is using ring dimension {cc.GetRingDimension()}\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalMultKeyGen(keys.secretKey)
# Input
x = [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
# Computing f(x) = x^18 + x^9 + 1
#
# Compare the following with the corresponding code
# for FIXEDAUTO. Here we need to track the depth of ciphertexts
# and call Rescale whenever needed. In this instance it's still
# not hard to do so, but this can be quite tedious in other
# complicated computations. (e.g. in bootstrapping)
#
#
# x^2
c2_depth2 = cc.EvalMult(c, c)
c2_depth1 = cc.Rescale(c2_depth2)
# x^4
c4_depth2 = cc.EvalMult(c2_depth1, c2_depth1)
c4_depth1 = cc.Rescale(c4_depth2)
# x^8
c8_depth2 = cc.EvalMult(c4_depth1, c4_depth1)
c8_depth1 = cc.Rescale(c8_depth2)
# x^16
c16_depth2 = cc.EvalMult(c8_depth1, c8_depth1)
c16_depth1 = cc.Rescale(c16_depth2)
# x^9
c9_depth2 = cc.EvalMult(c8_depth1, c)
# x^18
c18_depth2 = cc.EvalMult(c16_depth1, c2_depth1)
# Final result
c_res_depth2 = cc.EvalAdd(cc.EvalAdd(c18_depth2, c9_depth2), 1.0)
c_res_depth1 = cc.Rescale(c_res_depth2)
result = cc.Decrypt(c_res_depth1,keys.secretKey)
result.SetLength(batch_size)
print("x^18 + x^9 + 1 = ", result)
def hybrid_key_switching_demo1():
print("\n\n\n ===== hybrid_key_switching_demo1 ============= \n")
dnum = 2
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(5)
parameters.SetScalingModSize(90)
parameters.SetBatchSize(batch_size)
parameters.SetScalingTechnique(ScalingTechnique.FIXEDAUTO)
parameters.SetNumLargeDigits(dnum)
cc = GenCryptoContext(parameters)
print(f"CKKS scheme is using ring dimension {cc.GetRingDimension()}\n")
print(f"- Using HYBRID key switching with {dnum} digits\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalRotateKeyGen(keys.secretKey,[1,-2])
# Input
x = [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
t = time.time()
c_rot1 = cc.EvalRotate(c,1)
c_rot2 = cc.EvalRotate(c_rot1,-2)
time2digits = time.time() - t
result = cc.Decrypt(c_rot2,keys.secretKey)
result.SetLength(batch_size)
print(f"x rotate by -1 = {result}")
print(f" - 2 rotations with HYBRID (2 digits) took {time2digits*1000} ms")
def hybrid_key_switching_demo2():
print("\n\n\n ===== hybrid_key_switching_demo2 =============\n")
dnum = 3
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(5)
parameters.SetScalingModSize(90)
parameters.SetBatchSize(batch_size)
parameters.SetScalingTechnique(ScalingTechnique.FIXEDAUTO)
parameters.SetNumLargeDigits(dnum)
cc = GenCryptoContext(parameters)
# Compare the ring dimension in this demo to the one in the previous
print(f"CKKS scheme is using ring dimension {cc.GetRingDimension()}\n")
print(f"- Using HYBRID key switching with {dnum} digits\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalRotateKeyGen(keys.secretKey,[1,-2])
# Input
x = [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
t = time.time()
c_rot1 = cc.EvalRotate(c,1)
c_rot2 = cc.EvalRotate(c_rot1,-2)
time3digits = time.time() - t
# The runtime here is smaller than the previous demo
result = cc.Decrypt(c_rot2,keys.secretKey)
result.SetLength(batch_size)
print(f"x rotate by -1 = {result}")
print(f" - 2 rotations with HYBRID (3 digits) took {time3digits*1000} ms")
def fast_rotation_demo1():
print("\n\n\n ===== fast_rotation_demo1 =============\n")
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(1)
parameters.SetScalingModSize(90)
parameters.SetBatchSize(batch_size)
cc = GenCryptoContext(parameters)
N = cc.GetRingDimension()
print(f"CKKS scheme is using ring dimension {N}\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalRotateKeyGen(keys.secretKey,[1,2,3,4,5,6,7])
# Input
x = [0, 0, 0, 0, 0, 0, 0, 1]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
# First, we perform 7 regular (non-hoisted) rotations
# and measure the runtime
t = time.time()
c_rot1 = cc.EvalRotate(c,1)
c_rot2 = cc.EvalRotate(c,2)
c_rot3 = cc.EvalRotate(c,3)
c_rot4 = cc.EvalRotate(c,4)
c_rot5 = cc.EvalRotate(c,5)
c_rot6 = cc.EvalRotate(c,6)
c_rot7 = cc.EvalRotate(c,7)
time_no_hoisting = time.time() - t
c_res_no_hoist = c + c_rot1 + c_rot2 + c_rot3 + c_rot4 + c_rot5 + c_rot6 + c_rot7
# M is the cyclotomic order and we need it to call EvalFastRotation
M = 2*N
# Then, we perform 7 rotations with hoisting.
t = time.time()
c_precomp = cc.EvalFastRotationPrecompute(c)
c_rot1 = cc.EvalFastRotation(c, 1, M, c_precomp)
c_rot2 = cc.EvalFastRotation(c, 2, M, c_precomp)
c_rot3 = cc.EvalFastRotation(c, 3, M, c_precomp)
c_rot4 = cc.EvalFastRotation(c, 4, M, c_precomp)
c_rot5 = cc.EvalFastRotation(c, 5, M, c_precomp)
c_rot6 = cc.EvalFastRotation(c, 6, M, c_precomp)
c_rot7 = cc.EvalFastRotation(c, 7, M, c_precomp)
time_hoisting = time.time() - t
# The time with hoisting should be faster than without hoisting.
c_res_hoist = c + c_rot1 + c_rot2 + c_rot3 + c_rot4 + c_rot5 + c_rot6 + c_rot7
result = cc.Decrypt(c_res_no_hoist,keys.secretKey)
result.SetLength(batch_size)
print(f"Result without hoisting: {result}")
print(f" - 7 rotations without hoisting took {time_no_hoisting*1000} ms")
result = cc.Decrypt(c_res_hoist,keys.secretKey)
result.SetLength(batch_size)
print(f"Result with hoisting: {result}")
print(f" - 7 rotations with hoisting took {time_hoisting*1000} ms")
def fast_rotation_demo2():
print("\n\n\n ===== fast_rotation_demo2 =============\n")
batch_size = 8
parameters = CCParamsCKKSRNS()
parameters.SetMultiplicativeDepth(1)
parameters.SetScalingModSize(90)
parameters.SetBatchSize(batch_size)
parameters.SetScalingTechnique(ScalingTechnique.FIXEDAUTO)
parameters.SetKeySwitchTechnique(KeySwitchTechnique.BV)
digit_size = 10
first_mod_size = 100
parameters.SetFirstModSize(first_mod_size)
parameters.SetDigitSize(digit_size)
cc = GenCryptoContext(parameters)
N = cc.GetRingDimension()
print(f"CKKS scheme is using ring dimension {N}\n")
cc.Enable(PKESchemeFeature.PKE)
cc.Enable(PKESchemeFeature.KEYSWITCH)
cc.Enable(PKESchemeFeature.LEVELEDSHE)
keys = cc.KeyGen()
cc.EvalRotateKeyGen(keys.secretKey,[1,2,3,4,5,6,7])
# Input
x = [0, 0, 0, 0, 0, 0, 0, 1]
ptxt = cc.MakeCKKSPackedPlaintext(x)
print(f"Input x: {ptxt}")
c = cc.Encrypt(keys.publicKey,ptxt)
# First, we perform 7 regular (non-hoisted) rotations
# and measure the runtime
t = time.time()
c_rot1 = cc.EvalRotate(c,1)
c_rot2 = cc.EvalRotate(c,2)
c_rot3 = cc.EvalRotate(c,3)
c_rot4 = cc.EvalRotate(c,4)
c_rot5 = cc.EvalRotate(c,5)
c_rot6 = cc.EvalRotate(c,6)
c_rot7 = cc.EvalRotate(c,7)
time_no_hoisting = time.time() - t
c_res_no_hoist = c + c_rot1 + c_rot2 + c_rot3 + c_rot4 + c_rot5 + c_rot6 + c_rot7
# M is the cyclotomic order and we need it to call EvalFastRotation
M = 2*N
# Then, we perform 7 rotations with hoisting.
t = time.time()
c_precomp = cc.EvalFastRotationPrecompute(c)
c_rot1 = cc.EvalFastRotation(c, 1, M, c_precomp)
c_rot2 = cc.EvalFastRotation(c, 2, M, c_precomp)
c_rot3 = cc.EvalFastRotation(c, 3, M, c_precomp)
c_rot4 = cc.EvalFastRotation(c, 4, M, c_precomp)
c_rot5 = cc.EvalFastRotation(c, 5, M, c_precomp)
c_rot6 = cc.EvalFastRotation(c, 6, M, c_precomp)
c_rot7 = cc.EvalFastRotation(c, 7, M, c_precomp)
time_hoisting = time.time() - t
# The time with hoisting should be faster than without hoisting.
# Also, the benefits from hoisting should be more pronounced in this
# case because we're using BV. Of course, we also observe less
# accurate results than when using HYBRID, because of using
# digitSize = 10 (Users can decrease digitSize to see the accuracy
# increase, and performance decrease).
c_res_hoist = c + c_rot1 + c_rot2 + c_rot3 + c_rot4 + c_rot5 + c_rot6 + c_rot7
result = cc.Decrypt(c_res_no_hoist,keys.secretKey)
result.SetLength(batch_size)
print(f"Result without hoisting: {result}")
print(f" - 7 rotations without hoisting took {time_no_hoisting*1000} ms")
result = cc.Decrypt(c_res_hoist,keys.secretKey)
result.SetLength(batch_size)
print(f"Result with hoisting: {result}")
print(f" - 7 rotations with hoisting took {time_hoisting*1000} ms")
def main():
if get_native_int() == 128:
automatic_rescale_demo(ScalingTechnique.FIXEDAUTO)
# Note that FLEXIBLEAUTO is not supported for 128-bit CKKS
manual_rescale_demo(ScalingTechnique.FIXEDMANUAL)
hybrid_key_switching_demo1()
hybrid_key_switching_demo2()
fast_rotation_demo1()
fast_rotation_demo2()
else:
print("This demo only runs for 128-bit CKKS.\nIf you want to test it please reinstall the OpenFHE C++ with the flag -DNATIVE_INT=128, then reinstall OpenFHE-Python.")
if __name__ == "__main__":
main()