def test2():
img_path = '00001.jpg'
img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
print("Original image dimensions {}".format(img.shape))
sift = cv2.xfeatures2d.SIFT_create()
(kps, desc) = sift.detectAndCompute(img, None)
desc = preprocessing.normalize(np.array(
desc.flatten()[:DESC_SIZE]).reshape(1, DESC_SIZE),
norm='l2')
descriptor_vec1 = desc
descriptor_vec2 = descriptor_vec1
context = config()
public_key, secret_key = keygen(context)
encoder = IntegerEncoder(context.plain_modulus())
encryptor = Encryptor(context, public_key)
crtbuilder = PolyCRTBuilder(context)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
slot_count = (int)(crtbuilder.slot_count())
print("slot count {}".format(slot_count))
print("Plaintext shape", descriptor_vec1.shape)
plain_matrix = decompose_plain(slot_count, descriptor_vec1, crtbuilder)
for i in range(10000):
encrypted_matrix = Ciphertext()
print("Encrypting: ")
time_start = time.time()
encryptor.encrypt(plain_matrix, encrypted_matrix)
time_end = time.time()
print("Done in time {}".format((str)(1000 * (time_end - time_start))))
print("Square:")
time_start = time.time()
evaluator.square(encrypted_matrix)
time_end = time.time()
print("Square is done in {} miliseconds".format(
(str)(1000 * (time_end - time_start))))
plain_result = Plaintext()
print("Decryption plain: ")
time_start = time.time()
decryptor.decrypt(encrypted_matrix, plain_result)
time_end = time.time()
print("Decryption is done in {} miliseconds".format(
(str)(1000 * (time_end - time_start))))
# print("Plaintext polynomial: {}".format(plain_result.to_string()))
# print("Decoded integer: {}".format(encoder.decode_int32(plain_result)))
print("Noise budget {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
def example_ckks_basics():
print("Example: CKKS Basics");
#In this example we demonstrate evaluating a polynomial function
#
# PI*x^3 + 0.4*x + 1
#
#on encrypted floating-point input data x for a set of 4096 equidistant points
#in the interval [0, 1]. This example demonstrates many of the main features
#of the CKKS scheme, but also the challenges in using it.
#
# We start by setting up the CKKS scheme.
parms = EncryptionParameters(scheme_type.CKKS)
#We saw in `2_encoders.cpp' that multiplication in CKKS causes scales
#in ciphertexts to grow. The scale of any ciphertext must not get too close
#to the total size of coeff_modulus, or else the ciphertext simply runs out of
#room to store the scaled-up plaintext. The CKKS scheme provides a `rescale'
#functionality that can reduce the scale, and stabilize the scale expansion.
#
#Rescaling is a kind of modulus switch operation (recall `3_levels.cpp').
#As modulus switching, it removes the last of the primes from coeff_modulus,
#but as a side-effect it scales down the ciphertext by the removed prime.
#Usually we want to have perfect control over how the scales are changed,
#which is why for the CKKS scheme it is more common to use carefully selected
#primes for the coeff_modulus.
#
#More precisely, suppose that the scale in a CKKS ciphertext is S, and the
#last prime in the current coeff_modulus (for the ciphertext) is P. Rescaling
#to the next level changes the scale to S/P, and removes the prime P from the
#coeff_modulus, as usual in modulus switching. The number of primes limits
#how many rescalings can be done, and thus limits the multiplicative depth of
#the computation.
#
#It is possible to choose the initial scale freely. One good strategy can be
#to is to set the initial scale S and primes P_i in the coeff_modulus to be
#very close to each other. If ciphertexts have scale S before multiplication,
#they have scale S^2 after multiplication, and S^2/P_i after rescaling. If all
#P_i are close to S, then S^2/P_i is close to S again. This way we stabilize the
#scales to be close to S throughout the computation. Generally, for a circuit
#of depth D, we need to rescale D times, i.e., we need to be able to remove D
#primes from the coefficient modulus. Once we have only one prime left in the
#coeff_modulus, the remaining prime must be larger than S by a few bits to
#preserve the pre-decimal-point value of the plaintext.
#
#Therefore, a generally good strategy is to choose parameters for the CKKS
#scheme as follows:
#
# (1) Choose a 60-bit prime as the first prime in coeff_modulus. This will
# give the highest precision when decrypting;
# (2) Choose another 60-bit prime as the last element of coeff_modulus, as
# this will be used as the special prime and should be as large as the
# largest of the other primes;
# (3) Choose the intermediate primes to be close to each other.
#
#We use CoeffModulus::Create to generate primes of the appropriate size. Note
#that our coeff_modulus is 200 bits total, which is below the bound for our
#poly_modulus_degree: CoeffModulus::MaxBitCount(8192) returns 218.
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(CoeffModulus.Create(
poly_modulus_degree, IntVector([60, 40, 40, 60])))
#We choose the initial scale to be 2^40. At the last level, this leaves us
#60-40=20 bits of precision before the decimal point, and enough (roughly
#10-20 bits) of precision after the decimal point. Since our intermediate
#primes are 40 bits (in fact, they are very close to 2^40), we can achieve
#scale stabilization as described above.
scale = 2.0**40
context = SEALContext.Create(parms)
print_parameters(context)
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
relin_keys = keygen.relin_keys()
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
encoder = CKKSEncoder(context)
slot_count = encoder.slot_count()
print("Number of slots: {}".format(slot_count))
step_size = 1.0 / (slot_count - 1)
input = DoubleVector(list(map(lambda x: x*step_size, range(0, slot_count))))
print("Input vector: ")
print_vector(input)
print("Evaluating polynomial PI*x^3 + 0.4x + 1 ...")
#We create plaintexts for PI, 0.4, and 1 using an overload of CKKSEncoder::encode
#that encodes the given floating-point value to every slot in the vector.
plain_coeff3 = Plaintext()
plain_coeff1 = Plaintext()
plain_coeff0 = Plaintext()
encoder.encode(3.14159265, scale, plain_coeff3)
encoder.encode(0.4, scale, plain_coeff1)
encoder.encode(1.0, scale, plain_coeff0)
x_plain = Plaintext()
print("Encode input vectors.")
encoder.encode(input, scale, x_plain)
x1_encrypted = Ciphertext()
encryptor.encrypt(x_plain, x1_encrypted)
#To compute x^3 we first compute x^2 and relinearize. However, the scale has
#now grown to 2^80.
x3_encrypted = Ciphertext()
print("Compute x^2 and relinearize:")
evaluator.square(x1_encrypted, x3_encrypted)
evaluator.relinearize_inplace(x3_encrypted, relin_keys)
print(" + Scale of x^2 before rescale: {} bits".format(log2(x3_encrypted.scale())))
#Now rescale; in addition to a modulus switch, the scale is reduced down by
#a factor equal to the prime that was switched away (40-bit prime). Hence, the
#new scale should be close to 2^40. Note, however, that the scale is not equal
#to 2^40: this is because the 40-bit prime is only close to 2^40.
print("Rescale x^2.")
evaluator.rescale_to_next_inplace(x3_encrypted)
print(" + Scale of x^2 after rescale: {} bits".format(log2(x3_encrypted.scale())))
#Now x3_encrypted is at a different level than x1_encrypted, which prevents us
#from multiplying them to compute x^3. We could simply switch x1_encrypted to
#the next parameters in the modulus switching chain. However, since we still
#need to multiply the x^3 term with PI (plain_coeff3), we instead compute PI*x
#first and multiply that with x^2 to obtain PI*x^3. To this end, we compute
#PI*x and rescale it back from scale 2^80 to something close to 2^40.
print("Compute and rescale PI*x.")
x1_encrypted_coeff3 = Ciphertext()
evaluator.multiply_plain(x1_encrypted, plain_coeff3, x1_encrypted_coeff3)
print(" + Scale of PI*x before rescale: {} bits".format(log2(x1_encrypted_coeff3.scale())))
evaluator.rescale_to_next_inplace(x1_encrypted_coeff3)
print(" + Scale of PI*x after rescale: {} bits".format(log2(x1_encrypted_coeff3.scale())))
#Since x3_encrypted and x1_encrypted_coeff3 have the same exact scale and use
#the same encryption parameters, we can multiply them together. We write the
#result to x3_encrypted, relinearize, and rescale. Note that again the scale
#is something close to 2^40, but not exactly 2^40 due to yet another scaling
#by a prime. We are down to the last level in the modulus switching chain.
print("Compute, relinearize, and rescale (PI*x)*x^2.")
evaluator.multiply_inplace(x3_encrypted, x1_encrypted_coeff3)
evaluator.relinearize_inplace(x3_encrypted, relin_keys)
print(" + Scale of PI*x^3 before rescale: {} bits".format(log2(x3_encrypted.scale())))
evaluator.rescale_to_next_inplace(x3_encrypted)
print(" + Scale of PI*x^3 after rescale: {} bits".format(log2(x3_encrypted.scale())))
#Next we compute the degree one term. All this requires is one multiply_plain
#with plain_coeff1. We overwrite x1_encrypted with the result.
print("Compute and rescale 0.4*x.")
evaluator.multiply_plain_inplace(x1_encrypted, plain_coeff1)
print(" + Scale of 0.4*x before rescale: {} bits".format(log2(x1_encrypted.scale())))
evaluator.rescale_to_next_inplace(x1_encrypted)
print(" + Scale of 0.4*x after rescale: {} bits".format(log2(x1_encrypted.scale())))
#Now we would hope to compute the sum of all three terms. However, there is
#a serious problem: the encryption parameters used by all three terms are
#different due to modulus switching from rescaling.
#
#Encrypted addition and subtraction require that the scales of the inputs are
#the same, and also that the encryption parameters (parms_id) match. If there
#is a mismatch, Evaluator will throw an exception.
print("Parameters used by all three terms are different.")
print(" + Modulus chain index for x3_encrypted: {}".format(
context.get_context_data(x3_encrypted.parms_id()).chain_index()))
print(" + Modulus chain index for x1_encrypted: {}".format(
context.get_context_data(x1_encrypted.parms_id()).chain_index()))
print(" + Modulus chain index for plain_coeff0: {}".format(
context.get_context_data(plain_coeff0.parms_id()).chain_index()))
#Let us carefully consider what the scales are at this point. We denote the
#primes in coeff_modulus as P_0, P_1, P_2, P_3, in this order. P_3 is used as
#the special modulus and is not involved in rescalings. After the computations
#above the scales in ciphertexts are:
#
# - Product x^2 has scale 2^80 and is at level 2;
# - Product PI*x has scale 2^80 and is at level 2;
# - We rescaled both down to scale 2^80/P_2 and level 1;
# - Product PI*x^3 has scale (2^80/P_2)^2;
# - We rescaled it down to scale (2^80/P_2)^2/P_1 and level 0;
# - Product 0.4*x has scale 2^80;
# - We rescaled it down to scale 2^80/P_2 and level 1;
# - The contant term 1 has scale 2^40 and is at level 2.
#
#Although the scales of all three terms are approximately 2^40, their exact
#values are different, hence they cannot be added together.
print("The exact scales of all three terms are different:")
print(" + Exact scale in PI*x^3: {0:0.10f}".format(x3_encrypted.scale()))
print(" + Exact scale in 0.4*x: {0:0.10f}".format(x1_encrypted.scale()))
print(" + Exact scale in 1: {0:0.10f}".format(plain_coeff0.scale()))
#There are many ways to fix this problem. Since P_2 and P_1 are really close
#to 2^40, we can simply "lie" to Microsoft SEAL and set the scales to be the
#same. For example, changing the scale of PI*x^3 to 2^40 simply means that we
#scale the value of PI*x^3 by 2^120/(P_2^2*P_1), which is very close to 1.
#This should not result in any noticeable error.
#
#Another option would be to encode 1 with scale 2^80/P_2, do a multiply_plain
#with 0.4*x, and finally rescale. In this case we would need to additionally
#make sure to encode 1 with appropriate encryption parameters (parms_id).
#
#In this example we will use the first (simplest) approach and simply change
#the scale of PI*x^3 and 0.4*x to 2^40.
print("Normalize scales to 2^40.")
x3_encrypted.set_scale(2.0**40)
x1_encrypted.set_scale(2.0**40)
#We still have a problem with mismatching encryption parameters. This is easy
#to fix by using traditional modulus switching (no rescaling). CKKS supports
#modulus switching just like the BFV scheme, allowing us to switch away parts
#of the coefficient modulus when it is simply not needed.
print("Normalize encryption parameters to the lowest level.")
last_parms_id = x3_encrypted.parms_id()
evaluator.mod_switch_to_inplace(x1_encrypted, last_parms_id)
evaluator.mod_switch_to_inplace(plain_coeff0, last_parms_id)
#All three ciphertexts are now compatible and can be added.
print("Compute PI*x^3 + 0.4*x + 1.")
encrypted_result = Ciphertext()
evaluator.add(x3_encrypted, x1_encrypted, encrypted_result)
evaluator.add_plain_inplace(encrypted_result, plain_coeff0)
#First print the true result.
plain_result = Plaintext()
print("Decrypt and decode PI*x^3 + 0.4x + 1.")
print(" + Expected result:")
true_result = DoubleVector(list(map(lambda x: (3.14159265 * x * x + 0.4)* x + 1, input)))
print_vector(true_result)
#Decrypt, decode, and print the result.
decryptor.decrypt(encrypted_result, plain_result)
result = DoubleVector()
encoder.decode(plain_result, result)
print(" + Computed result ...... Correct.")
print_vector(result)
def example_bfv_basics():
print("Example: BFV Basics")
#In this example, we demonstrate performing simple computations (a polynomial
#evaluation) on encrypted integers using the BFV encryption scheme.
#
#The first task is to set up an instance of the EncryptionParameters class.
#It is critical to understand how the different parameters behave, how they
#affect the encryption scheme, performance, and the security level. There are
#three encryption parameters that are necessary to set:
#
# - poly_modulus_degree (degree of polynomial modulus);
# - coeff_modulus ([ciphertext] coefficient modulus);
# - plain_modulus (plaintext modulus; only for the BFV scheme).
#
#The BFV scheme cannot perform arbitrary computations on encrypted data.
#Instead, each ciphertext has a specific quantity called the `invariant noise
#budget' -- or `noise budget' for short -- measured in bits. The noise budget
#in a freshly encrypted ciphertext (initial noise budget) is determined by
#the encryption parameters. Homomorphic operations consume the noise budget
#at a rate also determined by the encryption parameters. In BFV the two basic
#operations allowed on encrypted data are additions and multiplications, of
#which additions can generally be thought of as being nearly free in terms of
#noise budget consumption compared to multiplications. Since noise budget
#consumption compounds in sequential multiplications, the most significant
#factor in choosing appropriate encryption parameters is the multiplicative
#depth of the arithmetic circuit that the user wants to evaluate on encrypted
#data. Once the noise budget of a ciphertext reaches zero it becomes too
#corrupted to be decrypted. Thus, it is essential to choose the parameters to
#be large enough to support the desired computation; otherwise the result is
#impossible to make sense of even with the secret key.
parms = EncryptionParameters(scheme_type.BFV)
#The first parameter we set is the degree of the `polynomial modulus'. This
#must be a positive power of 2, representing the degree of a power-of-two
#cyclotomic polynomial; it is not necessary to understand what this means.
#
#Larger poly_modulus_degree makes ciphertext sizes larger and all operations
#slower, but enables more complicated encrypted computations. Recommended
#values are 1024, 2048, 4096, 8192, 16384, 32768, but it is also possible
#to go beyond this range.
#
#In this example we use a relatively small polynomial modulus. Anything
#smaller than this will enable only very restricted encrypted computations.
poly_modulus_degree = 4096
parms.set_poly_modulus_degree(poly_modulus_degree)
#Next we set the [ciphertext] `coefficient modulus' (coeff_modulus). This
#parameter is a large integer, which is a product of distinct prime numbers,
#each up to 60 bits in size. It is represented as a vector of these prime
#numbers, each represented by an instance of the SmallModulus class. The
#bit-length of coeff_modulus means the sum of the bit-lengths of its prime
#factors.
#
#A larger coeff_modulus implies a larger noise budget, hence more encrypted
#computation capabilities. However, an upper bound for the total bit-length
#of the coeff_modulus is determined by the poly_modulus_degree, as follows:
#
# +----------------------------------------------------+
# | poly_modulus_degree | max coeff_modulus bit-length |
# +---------------------+------------------------------+
# | 1024 | 27 |
# | 2048 | 54 |
# | 4096 | 109 |
# | 8192 | 218 |
# | 16384 | 438 |
# | 32768 | 881 |
# +---------------------+------------------------------+
#
#These numbers can also be found in native/src/seal/util/hestdparms.h encoded
#in the function SEAL_HE_STD_PARMS_128_TC, and can also be obtained from the
#function
#
# CoeffModulus::MaxBitCount(poly_modulus_degree).
#
#For example, if poly_modulus_degree is 4096, the coeff_modulus could consist
#of three 36-bit primes (108 bits).
#
#Microsoft SEAL comes with helper functions for selecting the coeff_modulus.
#For new users the easiest way is to simply use
#
# CoeffModulus::BFVDefault(poly_modulus_degree),
#
#which returns std::vector<SmallModulus> consisting of a generally good choice
#for the given poly_modulus_degree.
parms.set_coeff_modulus(CoeffModulus.BFVDefault(poly_modulus_degree))
#The plaintext modulus can be any positive integer, even though here we take
#it to be a power of two. In fact, in many cases one might instead want it
#to be a prime number; we will see this in later examples. The plaintext
#modulus determines the size of the plaintext data type and the consumption
#of noise budget in multiplications. Thus, it is essential to try to keep the
#plaintext data type as small as possible for best performance. The noise
#budget in a freshly encrypted ciphertext is
#
# ~ log2(coeff_modulus/plain_modulus) (bits)
#
#and the noise budget consumption in a homomorphic multiplication is of the
#form log2(plain_modulus) + (other terms).
#
#The plaintext modulus is specific to the BFV scheme, and cannot be set when
#using the CKKS scheme.
parms.set_plain_modulus(1024)
#Now that all parameters are set, we are ready to construct a SEALContext
#object. This is a heavy class that checks the validity and properties of the
#parameters we just set.
context = SEALContext.Create(parms)
#Print the parameters that we have chosen.
print("Set encryption parameters and print")
print_parameters(context)
print("~~~~~~ A naive way to calculate 4(x^2+1)(x+1)^2. ~~~~~~")
#The encryption schemes in Microsoft SEAL are public key encryption schemes.
#For users unfamiliar with this terminology, a public key encryption scheme
#has a separate public key for encrypting data, and a separate secret key for
#decrypting data. This way multiple parties can encrypt data using the same
#shared public key, but only the proper recipient of the data can decrypt it
#with the secret key.
#
#We are now ready to generate the secret and public keys. For this purpose
#we need an instance of the KeyGenerator class. Constructing a KeyGenerator
#automatically generates the public and secret key, which can immediately be
#read to local variables.
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
#To be able to encrypt we need to construct an instance of Encryptor. Note
#that the Encryptor only requires the public key, as expected.
encryptor = Encryptor(context, public_key)
#Computations on the ciphertexts are performed with the Evaluator class. In
#a real use-case the Evaluator would not be constructed by the same party
#that holds the secret key.
evaluator = Evaluator(context)
#We will of course want to decrypt our results to verify that everything worked,
#so we need to also construct an instance of Decryptor. Note that the Decryptor
#requires the secret key.
decryptor = Decryptor(context, secret_key)
#As an example, we evaluate the degree 4 polynomial
#
# 4x^4 + 8x^3 + 8x^2 + 8x + 4
#
#over an encrypted x = 6. The coefficients of the polynomial can be considered
#as plaintext inputs, as we will see below. The computation is done modulo the
#plain_modulus 1024.
#
#While this examples is simple and easy to understand, it does not have much
#practical value. In later examples we will demonstrate how to compute more
#efficiently on encrypted integers and real or complex numbers.
#
#Plaintexts in the BFV scheme are polynomials of degree less than the degree
#of the polynomial modulus, and coefficients integers modulo the plaintext
#modulus. For readers with background in ring theory, the plaintext space is
#the polynomial quotient ring Z_T[X]/(X^N + 1), where N is poly_modulus_degree
#and T is plain_modulus.
#
#To get started, we create a plaintext containing the constant 6. For the
#plaintext element we use a constructor that takes the desired polynomial as
#a string with coefficients represented as hexadecimal numbers.
x = 6
x_plain = Plaintext(str(x))
print("Express x = {} as a plaintext polynomial 0x{}.".format(
x, x_plain.to_string()))
#We then encrypt the plaintext, producing a ciphertext.
x_encrypted = Ciphertext()
print("Encrypt x_plain to x_encrypted.")
encryptor.encrypt(x_plain, x_encrypted)
#In Microsoft SEAL, a valid ciphertext consists of two or more polynomials
#whose coefficients are integers modulo the product of the primes in the
#coeff_modulus. The number of polynomials in a ciphertext is called its `size'
#and is given by Ciphertext::size(). A freshly encrypted ciphertext always
#has size 2.
print(" + size of freshly encrypted x: {}".format(x_encrypted.size()))
#There is plenty of noise budget left in this freshly encrypted ciphertext.
print(" + noise budget in freshly encrypted x: {} bits".format(
decryptor.invariant_noise_budget(x_encrypted)))
#We decrypt the ciphertext and print the resulting plaintext in order to
#demonstrate correctness of the encryption.
x_decrypted = Plaintext()
decryptor.decrypt(x_encrypted, x_decrypted)
print(" + decryption of x_encrypted: 0x{} ...... Correct.".format(
x_decrypted.to_string()))
#When using Microsoft SEAL, it is typically advantageous to compute in a way
#that minimizes the longest chain of sequential multiplications. In other
#words, encrypted computations are best evaluated in a way that minimizes
#the multiplicative depth of the computation, because the total noise budget
#consumption is proportional to the multiplicative depth. For example, for
#our example computation it is advantageous to factorize the polynomial as
#
# 4x^4 + 8x^3 + 8x^2 + 8x + 4 = 4(x + 1)^2 * (x^2 + 1)
#
#to obtain a simple depth 2 representation. Thus, we compute (x + 1)^2 and
#(x^2 + 1) separately, before multiplying them, and multiplying by 4.
#
#First, we compute x^2 and add a plaintext "1". We can clearly see from the
#print-out that multiplication has consumed a lot of noise budget. The user
#can vary the plain_modulus parameter to see its effect on the rate of noise
#budget consumption.
print("Compute x_sq_plus_one (x^2+1).")
x_sq_plus_one = Ciphertext()
evaluator.square(x_encrypted, x_sq_plus_one)
plain_one = Plaintext("1")
evaluator.add_plain_inplace(x_sq_plus_one, plain_one)
#Encrypted multiplication results in the output ciphertext growing in size.
#More precisely, if the input ciphertexts have size M and N, then the output
#ciphertext after homomorphic multiplication will have size M+N-1. In this
#case we perform a squaring, and observe both size growth and noise budget
#consumption.
print(" + size of x_sq_plus_one: {}".format(x_sq_plus_one.size()))
print(" + noise budget in x_sq_plus_one: {} bits".format(
decryptor.invariant_noise_budget(x_sq_plus_one)))
#Even though the size has grown, decryption works as usual as long as noise
#budget has not reached 0.
decrypted_result = Plaintext()
decryptor.decrypt(x_sq_plus_one, decrypted_result)
print(" + decryption of x_sq_plus_one: 0x{} ...... Correct.".format(
decrypted_result.to_string()))
#Next, we compute (x + 1)^2.
print("Compute x_plus_one_sq ((x+1)^2).")
x_plus_one_sq = Ciphertext()
evaluator.add_plain(x_encrypted, plain_one, x_plus_one_sq)
evaluator.square_inplace(x_plus_one_sq)
print(" + size of x_plus_one_sq: {}".format(x_plus_one_sq.size()))
print(" + noise budget in x_plus_one_sq: {} bits".format(
decryptor.invariant_noise_budget(x_plus_one_sq)))
decryptor.decrypt(x_plus_one_sq, decrypted_result)
print(" + decryption of x_plus_one_sq: 0x{} ...... Correct.".format(
decrypted_result.to_string()))
#Finally, we multiply (x^2 + 1) * (x + 1)^2 * 4.
print("Compute encrypted_result (4(x^2+1)(x+1)^2).")
encrypted_result = Ciphertext()
plain_four = Plaintext("4")
evaluator.multiply_plain_inplace(x_sq_plus_one, plain_four)
evaluator.multiply(x_sq_plus_one, x_plus_one_sq, encrypted_result)
print(" + size of encrypted_result: {}".format(encrypted_result.size()))
print(" + noise budget in encrypted_result: {} bits".format(
decryptor.invariant_noise_budget(encrypted_result)))
print("NOTE: Decryption can be incorrect if noise budget is zero.")
print("~~~~~~ A better way to calculate 4(x^2+1)(x+1)^2. ~~~~~~")
#Noise budget has reached 0, which means that decryption cannot be expected
#to give the correct result. This is because both ciphertexts x_sq_plus_one
#and x_plus_one_sq consist of 3 polynomials due to the previous squaring
#operations, and homomorphic operations on large ciphertexts consume much more
#noise budget than computations on small ciphertexts. Computing on smaller
#ciphertexts is also computationally significantly cheaper.
#`Relinearization' is an operation that reduces the size of a ciphertext after
#multiplication back to the initial size, 2. Thus, relinearizing one or both
#input ciphertexts before the next multiplication can have a huge positive
#impact on both noise growth and performance, even though relinearization has
#a significant computational cost itself. It is only possible to relinearize
#size 3 ciphertexts down to size 2, so often the user would want to relinearize
#after each multiplication to keep the ciphertext sizes at 2.
#Relinearization requires special `relinearization keys', which can be thought
#of as a kind of public key. Relinearization keys can easily be created with
#the KeyGenerator.
#Relinearization is used similarly in both the BFV and the CKKS schemes, but
#in this example we continue using BFV. We repeat our computation from before,
#but this time relinearize after every multiplication.
#We use KeyGenerator::relin_keys() to create relinearization keys.
print("Generate relinearization keys.")
relin_keys = keygen.relin_keys()
#We now repeat the computation relinearizing after each multiplication.
print("Compute and relinearize x_squared (x^2),")
print("then compute x_sq_plus_one (x^2+1)")
x_squared = Ciphertext()
evaluator.square(x_encrypted, x_squared)
print(" + size of x_squared: {}".format(x_squared.size()))
evaluator.relinearize_inplace(x_squared, relin_keys)
print(" + size of x_squared (after relinearization): {}".format(
x_squared.size()))
evaluator.add_plain(x_squared, plain_one, x_sq_plus_one)
print(" + noise budget in x_sq_plus_one: {} bits".format(
decryptor.invariant_noise_budget(x_sq_plus_one)))
decryptor.decrypt(x_sq_plus_one, decrypted_result)
print(" + decryption of x_sq_plus_one: 0x{} ...... Correct.".format(
decrypted_result.to_string()))
x_plus_one = Ciphertext()
print("Compute x_plus_one (x+1),")
print("then compute and relinearize x_plus_one_sq ((x+1)^2).")
evaluator.add_plain(x_encrypted, plain_one, x_plus_one)
evaluator.square(x_plus_one, x_plus_one_sq)
print(" + size of x_plus_one_sq: {}".format(x_plus_one_sq.size()))
evaluator.relinearize_inplace(x_plus_one_sq, relin_keys)
print(" + noise budget in x_plus_one_sq: {} bits".format(
decryptor.invariant_noise_budget(x_plus_one_sq)))
decryptor.decrypt(x_plus_one_sq, decrypted_result)
print(" + decryption of x_plus_one_sq: 0x{} ...... Correct.".format(
decrypted_result.to_string()))
print("Compute and relinearize encrypted_result (4(x^2+1)(x+1)^2).")
evaluator.multiply_plain_inplace(x_sq_plus_one, plain_four)
evaluator.multiply(x_sq_plus_one, x_plus_one_sq, encrypted_result)
print(" + size of encrypted_result: {}".format(encrypted_result.size()))
evaluator.relinearize_inplace(encrypted_result, relin_keys)
print(" + size of encrypted_result (after relinearization): {}".format(
encrypted_result.size()))
print(" + noise budget in encrypted_result: {} bits".format(
decryptor.invariant_noise_budget(encrypted_result)))
print("NOTE: Notice the increase in remaining noise budget.")
#Relinearization clearly improved our noise consumption. We have still plenty
#of noise budget left, so we can expect the correct answer when decrypting.
print("Decrypt encrypted_result (4(x^2+1)(x+1)^2).")
decryptor.decrypt(encrypted_result, decrypted_result)
print(" + decryption of 4(x^2+1)(x+1)^2 = 0x{} ...... Correct.".format(
decrypted_result.to_string()))
def example_batching():
print_example_banner("Example: Batching with PolyCRTBuilder");
parms = EncryptionParameters()
parms.set_poly_modulus("1x^4096 + 1")
parms.set_coeff_modulus(seal.coeff_modulus_128(4096))
parms.set_plain_modulus(40961)
context = SEALContext(parms)
print_parameters(context)
qualifiers = context.qualifiers()
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
gal_keys = GaloisKeys()
keygen.generate_galois_keys(30, gal_keys)
#ev_keys = EvaluationKeys()
#keygen.generate_evaluation_keys(30, ev_keys)
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
crtbuilder = PolyCRTBuilder(context)
slot_count = (int)(crtbuilder.slot_count())
row_size = (int)(slot_count / 2)
print("Plaintext matrix row size: " + (str)(row_size))
def print_matrix(matrix):
print("")
print_size = 5
current_line = " ["
for i in range(print_size):
current_line += ((str)(matrix[i]) + ", ")
current_line += ("..., ")
for i in range(row_size - print_size, row_size):
current_line += ((str)(matrix[i]))
if i != row_size-1: current_line += ", "
else: current_line += "]"
print(current_line)
current_line = " ["
for i in range(row_size, row_size + print_size):
current_line += ((str)(matrix[i]) + ", ")
current_line += ("..., ")
for i in range(2*row_size - print_size, 2*row_size):
current_line += ((str)(matrix[i]))
if i != 2*row_size-1: current_line += ", "
else: current_line += "]"
print(current_line)
print("")
# [ 0, 1, 2, 3, 0, 0, ..., 0 ]
# [ 4, 5, 6, 7, 0, 0, ..., 0 ]
pod_matrix = [0]*slot_count
pod_matrix[0] = 0
pod_matrix[1] = 1
pod_matrix[2] = 2
pod_matrix[3] = 3
pod_matrix[row_size] = 4
pod_matrix[row_size + 1] = 5
pod_matrix[row_size + 2] = 6
pod_matrix[row_size + 3] = 7
print("Input plaintext matrix:")
print_matrix(pod_matrix)
plain_matrix = Plaintext()
crtbuilder.compose(pod_matrix, plain_matrix)
encrypted_matrix = Ciphertext()
print("Encrypting: ")
encryptor.encrypt(plain_matrix, encrypted_matrix)
print("Done")
print("Noise budget in fresh encryption: " +
(str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
pod_matrix2 = []
for i in range(slot_count): pod_matrix2.append((i % 2) + 1)
plain_matrix2 = Plaintext()
crtbuilder.compose(pod_matrix2, plain_matrix2)
print("Second input plaintext matrix:")
print_matrix(pod_matrix2)
print("Adding and squaring: ")
evaluator.add_plain(encrypted_matrix, plain_matrix2)
evaluator.square(encrypted_matrix)
evaluator.relinearize(encrypted_matrix, ev_keys)
print("Done")
print("Noise budget in result: " + (str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
plain_result = Plaintext()
print("Decrypting result: ")
decryptor.decrypt(encrypted_matrix, plain_result)
print("Done")
crtbuilder.decompose(plain_result)
pod_result = [plain_result.coeff_at(i) for i in range(plain_result.coeff_count())]
print("Result plaintext matrix:")
print_matrix(pod_result)
encryptor.encrypt(plain_matrix, encrypted_matrix)
print("Unrotated matrix: ")
print_matrix(pod_matrix)
print("Noise budget in fresh encryption: " +
(str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
# Now rotate the rows to the left 3 steps, decrypt, decompose, and print.
evaluator.rotate_rows(encrypted_matrix, 3, gal_keys)
print("Rotated rows 3 steps left: ")
decryptor.decrypt(encrypted_matrix, plain_result)
crtbuilder.decompose(plain_result)
pod_result = [plain_result.coeff_at(i) for i in range(plain_result.coeff_count())]
print_matrix(pod_result)
print("Noise budget after rotation" +
(str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
# Rotate columns (swap rows), decrypt, decompose, and print.
evaluator.rotate_columns(encrypted_matrix, gal_keys)
print("Rotated columns: ")
decryptor.decrypt(encrypted_matrix, plain_result)
crtbuilder.decompose(plain_result)
pod_result = [plain_result.coeff_at(i) for i in range(plain_result.coeff_count())]
print_matrix(pod_result)
print("Noise budget after rotation: " +
(str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
# Rotate rows to the right 4 steps, decrypt, decompose, and print.
evaluator.rotate_rows(encrypted_matrix, -4, gal_keys)
print("Rotated rows 4 steps right: ")
decryptor.decrypt(encrypted_matrix, plain_result)
crtbuilder.decompose(plain_result)
pod_result = [plain_result.coeff_at(i) for i in range(plain_result.coeff_count())]
print_matrix(pod_result)
print("Noise budget after rotation: " +
(str)(decryptor.invariant_noise_budget(encrypted_matrix)) + " bits")
print("\n[+] Proceding to homomorphic functions")
# dimension of X -> n (number of individuals) rows and 1+k (1+ number of covariates) cols
# dimension of y -> vector of length n (number of individuals)
# dimension of S -> n (number of individuals) rows and m (number of SNPs)
#restricting to 10 for calculation purposes
#########
y_encrypted = y_encrypted[:10]
k = len(X[0]) # k= 3
print("Y : ")
print_plain(y_encrypted)
for elementY in y_encrypted:
evaluator.square(elementY)
y_star2 = y_encrypted
del (y_encrypted)
print("\nY squared: ")
print_plain(y_star2)
print("\nrandom X : ")
print_plain(X)
X_star = matrixOperations.colSquare_Sum(X)
# dimension of S_star2 -> vector of length m (number of SNPs)
print("\nCol Squared X : ")
print_plain(X)
print_plain(X_star)
print("[=] Finished with homomorphic functions")
def example_basics_ii():
print_example_banner("Example: Basics II")
# In this example we explain what relinearization is, how to use it, and how
# it affects noise budget consumption.
# First we set the parameters, create a SEALContext, and generate the public
# and secret keys. We use slightly larger parameters than be fore to be able
# to do more homomorphic multiplications.
parms = EncryptionParameters()
parms.set_poly_modulus("1x^8192 + 1")
# The default coefficient modulus consists of the following primes:
# 0x7fffffffba0001,
# 0x7fffffffaa0001,
# 0x7fffffff7e0001,
# 0x3fffffffd60001.
# The total size is 219 bits.
parms.set_coeff_modulus(seal.coeff_modulus_128(8192))
parms.set_plain_modulus(1 << 10)
context = SEALContext(parms)
print_parameters(context)
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
# We also set up an Encryptor, Evaluator, and Decryptor here. We will
# encrypt polynomials directly in this example, so there is no need for
# an encoder.
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
# There are actually two more types of keys in SEAL: `evaluation keys' and
# `Galois keys'. Here we will discuss evaluation keys, and Galois keys will
# be discussed later in example_batching().
# In SEAL, a valid ciphertext consists of two or more polynomials with
# coefficients integers modulo the product of the primes in coeff_modulus.
# The current size of a ciphertext can be found using Ciphertext::size().
# A freshly encrypted ciphertext always has size 2.
#plain1 = Plaintext("1x^2 + 2x^1 + 3")
plain1 = Plaintext("1x^2 + 2x^1 + 3")
encrypted = Ciphertext()
print("")
print("Encrypting " + plain1.to_string() + ": ")
encryptor.encrypt(plain1, encrypted)
print("Done")
print("Size of a fresh encryption: " + (str)(encrypted.size()))
print("Noise budget in fresh encryption: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
# Homomorphic multiplication results in the output ciphertext growing in size.
# More precisely, if the input ciphertexts have size M and N, then the output
# ciphertext after homomorphic multiplication will have size M+N-1. In this
# case we square encrypted twice to observe this growth (also observe noise
# budget consumption).
evaluator.square(encrypted)
print("Size after squaring: " + (str)(encrypted.size()))
print("Noise budget after squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
plain2 = Plaintext()
decryptor.decrypt(encrypted, plain2)
print("Second power: " + plain2.to_string())
evaluator.square(encrypted)
print("Size after squaring: " + (str)(encrypted.size()))
print("Noise budget after squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
# It does not matter that the size has grown -- decryption works as usual.
# Observe from the print-out that the coefficients in the plaintext have
# grown quite large. One more squaring would cause some of them to wrap
# around plain_modulus (0x400), and as a result we would no longer obtain
# the expected result as an integer-coefficient polynomial. We can fix this
# problem to some extent by increasing plain_modulus. This would make sense,
# since we still have plenty of noise budget left.
plain2 = Plaintext()
decryptor.decrypt(encrypted, plain2)
print("Fourth power: " + plain2.to_string())
# The problem here is that homomorphic operations on large ciphertexts are
# computationally much more costly than on small ciphertexts. Specifically,
# homomorphic multiplication on input ciphertexts of size M and N will require
# O(M*N) polynomial multiplications to be performed, and an addition will
# require O(M+N) additions. Relinearization reduces the size of the ciphertexts
# after multiplication back to the initial size (2). Thus, relinearizing one
# or both inputs before the next multiplication, or e.g. before serializing the
# ciphertexts, can have a huge positive impact on performance.
# Another problem is that the noise budget consumption in multiplication is
# bigger when the input ciphertexts sizes are bigger. In a complicated
# computation the contribution of the sizes to the noise budget consumption
# can actually become the dominant term. We will point this out again below
# once we get to our example.
# Relinearization itself has both a computational cost and a noise budget cost.
# These both depend on a parameter called `decomposition bit count', which can
# be any integer at least 1 [dbc_min()] and at most 60 [dbc_max()]. A large
# decomposition bit count makes relinearization fast, but consumes more noise
# budget. A small decomposition bit count can make relinearization slower, but
# might not change the noise budget by any observable amount.
# Relinearization requires a special type of key called `evaluation keys'.
# These can be created by the KeyGenerator for any decomposition bit count.
# To relinearize a ciphertext of size M >= 2 back to size 2, we actually need
# M-2 evaluation keys. Attempting to relinearize a too large ciphertext with
# too few evaluation keys will result in an exception being thrown.
# We repeat our computation, but this time relinearize after both squarings.
# Since our ciphertext never grows past size 3 (we relinearize after every
# multiplication), it suffices to generate only one evaluation key.
# First, we need to create evaluation keys. We use a decomposition bit count
# of 16 here, which can be thought of as quite small.
ev_keys16 = EvaluationKeys()
# This function generates one single evaluation key. Another overload takes
# the number of keys to be generated as an argument, but one is all we need
# in this example (see above).
keygen.generate_evaluation_keys(16, ev_keys16)
print("")
print("Encrypting " + plain1.to_string() + ": ")
encryptor.encrypt(plain1, encrypted)
print("Done")
print("Size of a fresh encryption: " + (str)(encrypted.size()))
print("Noise budget in fresh encryption: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.square(encrypted)
print("Size after squaring: " + (str)(encrypted.size()))
print("Noise budget after squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.relinearize(encrypted, ev_keys16)
print("Size after relinearization: " + (str)(encrypted.size()))
print("Noise budget after relinearizing (dbs = " +
(str)(ev_keys16.decomposition_bit_count()) + "): " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.square(encrypted)
print("Size after second squaring: " + (str)(encrypted.size()) + " bits")
print("Noise budget after second squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.relinearize(encrypted, ev_keys16)
print("Size after relinearization: " + (str)(encrypted.size()))
print("Noise budget after relinearizing (dbs = " +
(str)(ev_keys16.decomposition_bit_count()) + "): " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
decryptor.decrypt(encrypted, plain2)
print("Fourth power: " + plain2.to_string())
# Of course the result is still the same, but this time we actually
# used less of our noise budget. This is not surprising for two reasons:
# - We used a very small decomposition bit count, which is why
# relinearization itself did not consume the noise budget by any
# observable amount;
# - Since our ciphertext sizes remain small throughout the two
# squarings, the noise budget consumption rate in multiplication
# remains as small as possible. Recall from above that operations
# on larger ciphertexts actually cause more noise growth.
# To make matters even more clear, we repeat the computation a third time,
# now using the largest possible decomposition bit count (60). We are not
# measuring the time here, but relinearization with these evaluation keys
# is significantly faster than with ev_keys16.
ev_keys60 = EvaluationKeys()
keygen.generate_evaluation_keys(seal.dbc_max(), ev_keys60)
print("")
print("Encrypting " + plain1.to_string() + ": ")
encryptor.encrypt(plain1, encrypted)
print("Done")
print("Size of a fresh encryption: " + (str)(encrypted.size()))
print("Noise budget in fresh encryption: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.square(encrypted)
print("Size after squaring: " + (str)(encrypted.size()))
print("Noise budget after squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.relinearize(encrypted, ev_keys60)
print("Size after relinearization: " + (str)(encrypted.size()))
print("Noise budget after relinearizing (dbc = " +
(str)(ev_keys60.decomposition_bit_count()) + "): " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.square(encrypted)
print("Size after second squaring: " + (str)(encrypted.size()))
print("Noise budget after second squaring: " +
(str)(decryptor.invariant_noise_budget) + " bits")
evaluator.relinearize(encrypted, ev_keys60)
print("Size after relinearization: " + (str)(encrypted.size()))
print("Noise budget after relinearizing (dbc = " +
(str)(ev_keys60.decomposition_bit_count()) + "): " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
decryptor.decrypt(encrypted, plain2)
print("Fourth power: " + plain2.to_string())
# Observe from the print-out that we have now used significantly more of our
# noise budget than in the two previous runs. This is again not surprising,
# since the first relinearization chops off a huge part of the noise budget.
# However, note that the second relinearization does not change the noise
# budget by any observable amount. This is very important to understand when
# optimal performance is desired: relinearization always drops the noise
# budget from the maximum (freshly encrypted ciphertext) down to a fixed
# amount depending on the encryption parameters and the decomposition bit
# count. On the other hand, homomorphic multiplication always consumes the
# noise budget from its current level. This is why the second relinearization
# does not change the noise budget anymore: it is already consumed past the
# fixed amount determinted by the decomposition bit count and the encryption
# parameters.
# We now perform a third squaring and observe an even further compounded
# decrease in the noise budget. Again, relinearization does not consume the
# noise budget at this point by any observable amount, even with the largest
# possible decomposition bit count.
evaluator.square(encrypted)
print("Size after third squaring " + (str)(encrypted.size()))
print("Noise budget after third squaring: " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
evaluator.relinearize(encrypted, ev_keys60)
print("Size after relinearization: " + (str)(encrypted.size()))
print("Noise budget after relinearizing (dbc = " +
(str)(ev_keys60.decomposition_bit_count()) + "): " +
(str)(decryptor.invariant_noise_budget(encrypted)) + " bits")
decryptor.decrypt(encrypted, plain2)
print("Eighth power: " + plain2.to_string())