def example_rotation_ckks():
print("Example: Rotation / Rotation in CKKS");
#Rotations in the CKKS scheme work very similarly to rotations in BFV.
parms = EncryptionParameters(scheme_type.CKKS)
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(CoeffModulus.Create(
poly_modulus_degree, IntVector([40, 40, 40, 40, 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()
gal_keys = keygen.galois_keys()
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
ckks_encoder = CKKSEncoder(context)
slot_count = ckks_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_vector(input)
scale = 2.0**50
print("Encode and encrypt.")
plain = Plaintext()
ckks_encoder.encode(input, scale, plain)
encrypted = Ciphertext()
encryptor.encrypt(plain, encrypted)
rotated = Ciphertext()
print("Rotate 2 steps left.")
evaluator.rotate_vector(encrypted, 2, gal_keys, rotated)
print(" + Decrypt and decode ...... Correct.")
decryptor.decrypt(rotated, plain)
result = DoubleVector()
ckks_encoder.decode(plain, result)
print_vector(result)
def example_rotation_bfv():
print("Example: Rotation / Rotation in BFV")
parms = EncryptionParameters(scheme_type.BFV)
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(CoeffModulus.BFVDefault(poly_modulus_degree))
parms.set_plain_modulus(PlainModulus.Batching(poly_modulus_degree, 20))
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)
batch_encoder = BatchEncoder(context)
slot_count = batch_encoder.slot_count()
row_size = int(slot_count / 2)
print("Plaintext matrix row size: {}".format(row_size))
pod_matrix = UInt64Vector([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, row_size)
#First we use BatchEncoder to encode the matrix into a plaintext. We encrypt
#the plaintext as usual.
plain_matrix = Plaintext()
print("Encode and encrypt.")
batch_encoder.encode(pod_matrix, plain_matrix)
encrypted_matrix = Ciphertext()
encryptor.encrypt(plain_matrix, encrypted_matrix)
print(" + Noise budget in fresh encryption: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
#Rotations require yet another type of special key called `Galois keys'. These
#are easily obtained from the KeyGenerator.
gal_keys = keygen.galois_keys()
#Now rotate both matrix rows 3 steps to the left, decrypt, decode, and print.
print("Rotate rows 3 steps left.")
evaluator.rotate_rows_inplace(encrypted_matrix, 3, gal_keys)
plain_result = Plaintext()
print(" + Noise budget after rotation: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
print(" + Decrypt and decode ...... Correct.")
decryptor.decrypt(encrypted_matrix, plain_result)
batch_encoder.decode(plain_result, pod_matrix)
print_matrix(pod_matrix, row_size)
#We can also rotate the columns, i.e., swap the rows.
print("Rotate columns.")
evaluator.rotate_columns_inplace(encrypted_matrix, gal_keys)
print(" + Noise budget after rotation: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
print(" + Decrypt and decode ...... Correct.")
decryptor.decrypt(encrypted_matrix, plain_result)
batch_encoder.decode(plain_result, pod_matrix)
print_matrix(pod_matrix, row_size)
#Finally, we rotate the rows 4 steps to the right, decrypt, decode, and print.
print("Rotate rows 4 steps right.")
evaluator.rotate_rows_inplace(encrypted_matrix, -4, gal_keys)
print(" + Noise budget after rotation: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
print(" + Decrypt and decode ...... Correct.")
decryptor.decrypt(encrypted_matrix, plain_result)
batch_encoder.decode(plain_result, pod_matrix)
print_matrix(pod_matrix, row_size)
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_levels():
print("Example: Levels")
#In this examples we describe the concept of `levels' in BFV and CKKS and the
#related objects that represent them in Microsoft SEAL.
#
#In Microsoft SEAL a set of encryption parameters (excluding the random number
#generator) is identified uniquely by a 256-bit hash of the parameters. This
#hash is called the `parms_id' and can be easily accessed and printed at any
#time. The hash will change as soon as any of the parameters is changed.
#
#When a SEALContext is created from a given EncryptionParameters instance,
#Microsoft SEAL automatically creates a so-called `modulus switching chain',
#which is a chain of other encryption parameters derived from the original set.
#The parameters in the modulus switching chain are the same as the original
#parameters with the exception that size of the coefficient modulus is
#decreasing going down the chain. More precisely, each parameter set in the
#chain attempts to remove the last coefficient modulus prime from the
#previous set; this continues until the parameter set is no longer valid
#(e.g., plain_modulus is larger than the remaining coeff_modulus). It is easy
#to walk through the chain and access all the parameter sets. Additionally,
#each parameter set in the chain has a `chain index' that indicates its
#position in the chain so that the last set has index 0. We say that a set
#of encryption parameters, or an object carrying those encryption parameters,
#is at a higher level in the chain than another set of parameters if its the
#chain index is bigger, i.e., it is earlier in the chain.
#
#Each set of parameters in the chain involves unique pre-computations performed
#when the SEALContext is created, and stored in a SEALContext::ContextData
#object. The chain is basically a linked list of SEALContext::ContextData
#objects, and can easily be accessed through the SEALContext at any time. Each
#node can be identified by the parms_id of its specific encryption parameters
#(poly_modulus_degree remains the same but coeff_modulus varies).
parms = EncryptionParameters(scheme_type.BFV)
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
#In this example we use a custom coeff_modulus, consisting of 5 primes of
#sizes 50, 30, 30, 50, and 50 bits. Note that this is still OK according to
#the explanation in `1_bfv_basics.cpp'. Indeed,
#
# CoeffModulus::MaxBitCount(poly_modulus_degree)
#
#returns 218 (greater than 50+30+30+50+50=210).
#
#Due to the modulus switching chain, the order of the 5 primes is significant.
#The last prime has a special meaning and we call it the `special prime'. Thus,
#the first parameter set in the modulus switching chain is the only one that
#involves the special prime. All key objects, such as SecretKey, are created
#at this highest level. All data objects, such as Ciphertext, can be only at
#lower levels. The special prime should be as large as the largest of the
#other primes in the coeff_modulus, although this is not a strict requirement.
#
# special prime +---------+
# |
# v
#coeff_modulus: { 50, 30, 30, 50, 50 } +---+ Level 4 (all keys; `key level')
# |
# |
# coeff_modulus: { 50, 30, 30, 50 } +---+ Level 3 (highest `data level')
# |
# |
# coeff_modulus: { 50, 30, 30 } +---+ Level 2
# |
# |
# coeff_modulus: { 50, 30 } +---+ Level 1
# |
# |
# coeff_modulus: { 50 } +---+ Level 0 (lowest level)
parms.set_coeff_modulus(
CoeffModulus.Create(poly_modulus_degree,
IntVector([50, 30, 30, 50, 50])))
#In this example the plain_modulus does not play much of a role; we choose
#some reasonable value.
parms.set_plain_modulus(PlainModulus.Batching(poly_modulus_degree, 20))
context = SEALContext.Create(parms)
print_parameters(context)
#There are convenience method for accessing the SEALContext::ContextData for
#some of the most important levels:
#
# SEALContext::key_context_data(): access to key level ContextData
# SEALContext::first_context_data(): access to highest data level ContextData
# SEALContext::last_context_data(): access to lowest level ContextData
#
#We iterate over the chain and print the parms_id for each set of parameters.
print("Print the modulus switching chain.")
#First print the key level parameter information.
context_data = context.key_context_data()
print("----> Level (chain index): {} ...... key_context_data()".format(
context_data.chain_index()))
print(" parms_id: {}".format(list2hex(context_data.parms_id())))
print("coeff_modulus primes:", end=' ')
for prime in context_data.parms().coeff_modulus():
print(hex(prime.value()), end=' ')
print("")
print("\\")
print(" \\-->")
#Next iterate over the remaining (data) levels.
context_data = context.first_context_data()
while (context_data):
print(" Level (chain index): {} ".format(context_data.chain_index()),
end='')
if context_data.parms_id() == context.first_parms_id():
print(" ...... first_context_data()")
elif context_data.parms_id() == context.last_parms_id():
print(" ...... last_context_data()")
else:
print("")
print(" parms_id: {}".format(list2hex(context_data.parms_id())))
print("coeff_modulus primes:", end=' ')
for prime in context_data.parms().coeff_modulus():
print(hex(prime.value()), end=' ')
print("")
print("\\")
print(" \\-->")
#Step forward in the chain.
context_data = context_data.next_context_data()
print(" End of chain reached")
#We create some keys and check that indeed they appear at the highest level.
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
relin_keys = keygen.relin_keys()
galois_keys = keygen.galois_keys()
print("Print the parameter IDs of generated elements.")
print(" + public_key: {}".format(list2hex(public_key.parms_id())))
print(" + secret_key: {}".format(list2hex(secret_key.parms_id())))
print(" + relin_keys: {}".format(list2hex(relin_keys.parms_id())))
print(" + galois_keys: {}".format(list2hex(galois_keys.parms_id())))
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
#In the BFV scheme plaintexts do not carry a parms_id, but ciphertexts do. Note
#how the freshly encrypted ciphertext is at the highest data level.
plain = Plaintext("1x^3 + 2x^2 + 3x^1 + 4")
encrypted = Ciphertext()
encryptor.encrypt(plain, encrypted)
print(" + plain: {} (not set in BFV)".format(
list2hex(plain.parms_id())))
print(" + encrypted: {}".format(list2hex(encrypted.parms_id())))
#`Modulus switching' is a technique of changing the ciphertext parameters down
#in the chain. The function Evaluator::mod_switch_to_next always switches to
#the next level down the chain, whereas Evaluator::mod_switch_to switches to
#a parameter set down the chain corresponding to a given parms_id. However, it
#is impossible to switch up in the chain.
print("Perform modulus switching on encrypted and print.")
context_data = context.first_context_data()
print("---->")
while (context_data.next_context_data()):
print(" Level (chain index): {} ".format(context_data.chain_index()))
print(" parms_id of encrypted: {}".format(
list2hex(encrypted.parms_id())))
print(" Noise budget at this level: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
print("\\")
print(" \\-->")
evaluator.mod_switch_to_next_inplace(encrypted)
context_data = context_data.next_context_data()
print(" Level (chain index): {}".format(context_data.chain_index()))
print(" parms_id of encrypted: {}".format(encrypted.parms_id()))
print(" Noise budget at this level: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
print("\\")
print(" \\-->")
print(" End of chain reached")
#At this point it is hard to see any benefit in doing this: we lost a huge
#amount of noise budget (i.e., computational power) at each switch and seemed
#to get nothing in return. Decryption still works.
print("Decrypt still works after modulus switching.")
decryptor.decrypt(encrypted, plain)
print(" + Decryption of encrypted: {} ...... Correct.".format(
plain.to_string()))
#However, there is a hidden benefit: the size of the ciphertext depends
#linearly on the number of primes in the coefficient modulus. Thus, if there
#is no need or intention to perform any further computations on a given
#ciphertext, we might as well switch it down to the smallest (last) set of
#parameters in the chain before sending it back to the secret key holder for
#decryption.
#
#Also the lost noise budget is actually not an issue at all, if we do things
#right, as we will see below.
#
#First we recreate the original ciphertext and perform some computations.
print("Computation is more efficient with modulus switching.")
print("Compute the 8th power.")
encryptor.encrypt(plain, encrypted)
print(" + Noise budget fresh: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
evaluator.square_inplace(encrypted)
evaluator.relinearize_inplace(encrypted, relin_keys)
print(" + Noise budget of the 2nd power: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
evaluator.square_inplace(encrypted)
evaluator.relinearize_inplace(encrypted, relin_keys)
print(" + Noise budget of the 4th power: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
#Surprisingly, in this case modulus switching has no effect at all on the
#noise budget.
evaluator.mod_switch_to_next_inplace(encrypted)
print(" + Noise budget after modulus switching: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
#This means that there is no harm at all in dropping some of the coefficient
#modulus after doing enough computations. In some cases one might want to
#switch to a lower level slightly earlier, actually sacrificing some of the
#noise budget in the process, to gain computational performance from having
#smaller parameters. We see from the print-out that the next modulus switch
#should be done ideally when the noise budget is down to around 25 bits.
evaluator.square_inplace(encrypted)
evaluator.relinearize_inplace(encrypted, relin_keys)
print(" + Noise budget of the 8th power: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
evaluator.mod_switch_to_next_inplace(encrypted)
print(" + Noise budget after modulus switching: {} bits".format(
decryptor.invariant_noise_budget(encrypted)))
#At this point the ciphertext still decrypts correctly, has very small size,
#and the computation was as efficient as possible. Note that the decryptor
#can be used to decrypt a ciphertext at any level in the modulus switching
#chain.
decryptor.decrypt(encrypted, plain)
print(" + Decryption of the 8th power (hexadecimal) ...... Correct.")
print(" {}".format(plain.to_string()))
#In BFV modulus switching is not necessary and in some cases the user might
#not want to create the modulus switching chain, except for the highest two
#levels. This can be done by passing a bool `false' to SEALContext::Create.
context = SEALContext.Create(parms, False)
#We can check that indeed the modulus switching chain has been created only
#for the highest two levels (key level and highest data level). The following
#loop should execute only once.
print("Optionally disable modulus switching chain expansion.")
print("Print the modulus switching chain.")
print("---->")
context_data = context.key_context_data()
while (context_data):
print(" Level (chain index): {}".format(context_data.chain_index()))
print(" parms_id: {}".format(list2hex(context_data.parms_id())))
print("coeff_modulus primes:", end=' ')
for prime in context_data.parms().coeff_modulus():
print(hex(prime.value()), end=' ')
print("")
print("\\")
print(" \\-->")
context_data = context_data.next_context_data()
print(" End of chain reached")
def example_integer_encoder():
print("Example: Encoders / Integer Encoder")
#[IntegerEncoder] (For BFV scheme only)
#
#The IntegerEncoder encodes integers to BFV plaintext polynomials as follows.
#First, a binary expansion of the integer is computed. Next, a polynomial is
#created with the bits as coefficients. For example, the integer
#
# 26 = 2^4 + 2^3 + 2^1
#
#is encoded as the polynomial 1x^4 + 1x^3 + 1x^1. Conversely, plaintext
#polynomials are decoded by evaluating them at x=2. For negative numbers the
#IntegerEncoder simply stores all coefficients as either 0 or -1, where -1 is
#represented by the unsigned integer plain_modulus - 1 in memory.
#
#Since encrypted computations operate on the polynomials rather than on the
#encoded integers themselves, the polynomial coefficients will grow in the
#course of such computations. For example, computing the sum of the encrypted
#encoded integer 26 with itself will result in an encrypted polynomial with
#larger coefficients: 2x^4 + 2x^3 + 2x^1. Squaring the encrypted encoded
#integer 26 results also in increased coefficients due to cross-terms, namely,
#
# (2x^4 + 2x^3 + 2x^1)^2 = 1x^8 + 2x^7 + 1x^6 + 2x^5 + 2x^4 + 1x^2;
#
#further computations will quickly increase the coefficients much more.
#Decoding will still work correctly in this case (evaluating the polynomial
#at x=2), but since the coefficients of plaintext polynomials are really
#integers modulo plain_modulus, implicit reduction modulo plain_modulus may
#yield unexpected results. For example, adding 1x^4 + 1x^3 + 1x^1 to itself
#plain_modulus many times will result in the constant polynomial 0, which is
#clearly not equal to 26 * plain_modulus. It can be difficult to predict when
#such overflow will take place especially when computing several sequential
#multiplications.
#
#The IntegerEncoder is easy to understand and use for simple computations,
#and can be a good tool to experiment with for users new to Microsoft SEAL.
#However, advanced users will probably prefer more efficient approaches,
#such as the BatchEncoder or the CKKSEncoder.
parms = EncryptionParameters(scheme_type.BFV)
poly_modulus_degree = 4096
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(CoeffModulus.BFVDefault(poly_modulus_degree))
#There is no hidden logic behind our choice of the plain_modulus. The only
#thing that matters is that the plaintext polynomial coefficients will not
#exceed this value at any point during our computation; otherwise the result
#will be incorrect.
parms.set_plain_modulus(512)
context = SEALContext.Create(parms)
print_parameters(context)
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
#We create an IntegerEncoder.
encoder = IntegerEncoder(context)
#First, we encode two integers as plaintext polynomials. Note that encoding
#is not encryption: at this point nothing is encrypted.
value1 = 5
plain1 = encoder.encode(value1)
print("Encode {} as polynomial {} (plain1), ".format(
value1, plain1.to_string()))
value2 = -7
plain2 = encoder.encode(value2)
print(" encode {} as polynomial {} (plain2)".format(
value2, plain2.to_string()))
#Now we can encrypt the plaintext polynomials.
encrypted1 = Ciphertext()
encrypted2 = Ciphertext()
print("Encrypt plain1 to encrypted1 and plain2 to encrypted2.")
encryptor.encrypt(plain1, encrypted1)
encryptor.encrypt(plain2, encrypted2)
print(" + Noise budget in encrypted1: {} bits".format(
decryptor.invariant_noise_budget(encrypted1)))
print(" + Noise budget in encrypted2: {} bits".format(
decryptor.invariant_noise_budget(encrypted2)))
#As a simple example, we compute (-encrypted1 + encrypted2) * encrypted2.
encryptor.encrypt(plain2, encrypted2)
encrypted_result = Ciphertext()
print(
"Compute encrypted_result = (-encrypted1 + encrypted2) * encrypted2.")
evaluator.negate(encrypted1, encrypted_result)
evaluator.add_inplace(encrypted_result, encrypted2)
evaluator.multiply_inplace(encrypted_result, encrypted2)
print(" + Noise budget in encrypted_result: {} bits".format(
decryptor.invariant_noise_budget(encrypted_result)))
plain_result = Plaintext()
print("Decrypt encrypted_result to plain_result.")
decryptor.decrypt(encrypted_result, plain_result)
#Print the result plaintext polynomial. The coefficients are not even close
#to exceeding our plain_modulus, 512.
print(" + Plaintext polynomial: {}".format(plain_result.to_string()))
#Decode to obtain an integer result.
print("Decode plain_result.")
print(" + Decoded integer: {} ...... Correct.".format(
encoder.decode_int32(plain_result)))
def example_ckks_encoder():
print("Example: Encoders / CKKS Encoder")
#[CKKSEncoder] (For CKKS scheme only)
#
#In this example we demonstrate the Cheon-Kim-Kim-Song (CKKS) scheme for
#computing on encrypted real or complex numbers. We start by creating
#encryption parameters for the CKKS scheme. There are two important
#differences compared to the BFV scheme:
#
# (1) CKKS does not use the plain_modulus encryption parameter;
# (2) Selecting the coeff_modulus in a specific way can be very important
# when using the CKKS scheme. We will explain this further in the file
# `ckks_basics.cpp'. In this example we use CoeffModulus::Create to
# generate 5 40-bit prime numbers.
parms = EncryptionParameters(scheme_type.CKKS)
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(
CoeffModulus.Create(poly_modulus_degree,
IntVector([40, 40, 40, 40, 40])))
#We create the SEALContext as usual and print the parameters.
context = SEALContext.Create(parms)
print_parameters(context)
#Keys are created the same way as for the BFV scheme.
keygen = KeyGenerator(context)
public_key = keygen.public_key()
secret_key = keygen.secret_key()
relin_keys = keygen.relin_keys()
#We also set up an Encryptor, Evaluator, and Decryptor as usual.
encryptor = Encryptor(context, public_key)
evaluator = Evaluator(context)
decryptor = Decryptor(context, secret_key)
#To create CKKS plaintexts we need a special encoder: there is no other way
#to create them. The IntegerEncoder and BatchEncoder cannot be used with the
#CKKS scheme. The CKKSEncoder encodes vectors of real or complex numbers into
#Plaintext objects, which can subsequently be encrypted. At a high level this
#looks a lot like what BatchEncoder does for the BFV scheme, but the theory
#behind it is completely different.
encoder = CKKSEncoder(context)
#In CKKS the number of slots is poly_modulus_degree / 2 and each slot encodes
#one real or complex number. This should be contrasted with BatchEncoder in
#the BFV scheme, where the number of slots is equal to poly_modulus_degree
#and they are arranged into a matrix with two rows.
slot_count = encoder.slot_count()
print("Number of slots: {}".format(slot_count))
#We create a small vector to encode; the CKKSEncoder will implicitly pad it
#with zeros to full size (poly_modulus_degree / 2) when encoding.
input = DoubleVector([0.0, 1.1, 2.2, 3.3])
print("Input vector: ")
print_vector(input)
#Now we encode it with CKKSEncoder. The floating-point coefficients of `input'
#will be scaled up by the parameter `scale'. This is necessary since even in
#the CKKS scheme the plaintext elements are fundamentally polynomials with
#integer coefficients. It is instructive to think of the scale as determining
#the bit-precision of the encoding; naturally it will affect the precision of
#the result.
#
#In CKKS the message is stored modulo coeff_modulus (in BFV it is stored modulo
#plain_modulus), so the scaled message must not get too close to the total size
#of coeff_modulus. In this case our coeff_modulus is quite large (200 bits) so
#we have little to worry about in this regard. For this simple example a 30-bit
#scale is more than enough.
plain = Plaintext()
scale = 2.0**30
print("Encode input vector.")
encoder.encode(input, scale, plain)
#We can instantly decode to check the correctness of encoding.
output = DoubleVector()
print(" + Decode input vector ...... Correct.")
encoder.decode(plain, output)
print_vector(output)
#The vector is encrypted the same was as in BFV.
encrypted = Ciphertext()
print("Encrypt input vector, square, and relinearize.")
encryptor.encrypt(plain, encrypted)
#Basic operations on the ciphertexts are still easy to do. Here we square the
#ciphertext, decrypt, decode, and print the result. We note also that decoding
#returns a vector of full size (poly_modulus_degree / 2); this is because of
#the implicit zero-padding mentioned above.
evaluator.square_inplace(encrypted)
evaluator.relinearize_inplace(encrypted, relin_keys)
#We notice that the scale in the result has increased. In fact, it is now the
#square of the original scale: 2^60.
print(" + Scale in squared input: {} ( {} bits)".format(
encrypted.scale(), log2(encrypted.scale())))
print("Decrypt and decode.")
decryptor.decrypt(encrypted, plain)
encoder.decode(plain, output)
print(" + Result vector ...... Correct.")
print_vector(output)
def example_batch_encoder():
print("Example: Encoders / Batch Encoder")
#[BatchEncoder] (For BFV scheme only)
#
#Let N denote the poly_modulus_degree and T denote the plain_modulus. Batching
#allows the BFV plaintext polynomials to be viewed as 2-by-(N/2) matrices, with
#each element an integer modulo T. In the matrix view, encrypted operations act
#element-wise on encrypted matrices, allowing the user to obtain speeds-ups of
#several orders of magnitude in fully vectorizable computations. Thus, in all
#but the simplest computations, batching should be the preferred method to use
#with BFV, and when used properly will result in implementations outperforming
#anything done with the IntegerEncoder.
parms = EncryptionParameters(scheme_type.BFV)
poly_modulus_degree = 8192
parms.set_poly_modulus_degree(poly_modulus_degree)
parms.set_coeff_modulus(CoeffModulus.BFVDefault(poly_modulus_degree))
#To enable batching, we need to set the plain_modulus to be a prime number
#congruent to 1 modulo 2*poly_modulus_degree. Microsoft SEAL provides a helper
#method for finding such a prime. In this example we create a 20-bit prime
#that supports batching.
parms.set_plain_modulus(PlainModulus.Batching(poly_modulus_degree, 20))
context = SEALContext.Create(parms)
print_parameters(context)
#We can verify that batching is indeed enabled by looking at the encryption
#parameter qualifiers created by SEALContext.
##HERE
qualifiers = context.qualifiers()
print("Batching enabled: {}".format(qualifiers.using_batching))
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)
#Batching is done through an instance of the BatchEncoder class.
batch_encoder = BatchEncoder(context)
#The total number of batching `slots' equals the poly_modulus_degree, N, and
#these slots are organized into 2-by-(N/2) matrices that can be encrypted and
#computed on. Each slot contains an integer modulo plain_modulus.
slot_count = batch_encoder.slot_count()
row_size = int(slot_count / 2)
print("Plaintext matrix row size: {}".format(row_size))
#The matrix plaintext is simply given to BatchEncoder as a flattened vector
#of numbers. The first `row_size' many numbers form the first row, and the
#rest form the second row. Here we create the following matrix:
#
# [ 0, 1, 2, 3, 0, 0, ..., 0 ]
# [ 4, 5, 6, 7, 0, 0, ..., 0 ]
pod_matrix = Int64Vector([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, row_size)
#First we use BatchEncoder to encode the matrix into a plaintext polynomial.
plain_matrix = Plaintext()
print("Encode plaintext matrix:")
batch_encoder.encode(pod_matrix, plain_matrix)
#We can instantly decode to verify correctness of the encoding. Note that no
#encryption or decryption has yet taken place.
print(" + Decode plaintext matrix ...... Correct.")
pod_result = Int64Vector([0] * slot_count)
batch_encoder.decode(plain_matrix, pod_result)
print_matrix(pod_result, row_size)
#Next we encrypt the encoded plaintext.
encrypted_matrix = Ciphertext()
print("Encrypt plain_matrix to encrypted_matrix.")
encryptor.encrypt(plain_matrix, encrypted_matrix)
print(" + Noise budget in encrypted_matrix: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
#Operating on the ciphertext results in homomorphic operations being performed
#simultaneously in all 8192 slots (matrix elements). To illustrate this, we
#form another plaintext matrix
#
# [ 1, 2, 1, 2, 1, 2, ..., 2 ]
# [ 1, 2, 1, 2, 1, 2, ..., 2 ]
#
#and encode it into a plaintext.
pod_matrix2 = UInt64Vector([1, 2] * row_size)
plain_matrix2 = Plaintext()
batch_encoder.encode(pod_matrix2, plain_matrix2)
print("Second input plaintext matrix:")
print_matrix(pod_matrix2, row_size)
#We now add the second (plaintext) matrix to the encrypted matrix, and square
#the sum.
print("Sum, square, and relinearize.")
evaluator.add_plain_inplace(encrypted_matrix, plain_matrix2)
evaluator.square_inplace(encrypted_matrix)
evaluator.relinearize_inplace(encrypted_matrix, relin_keys)
#How much noise budget do we have left?
print(" + Noise budget in result: {} bits".format(
decryptor.invariant_noise_budget(encrypted_matrix)))
#We decrypt and decompose the plaintext to recover the result as a matrix.
plain_result = Plaintext()
print("Decrypt and decode result.")
decryptor.decrypt(encrypted_matrix, plain_result)
batch_encoder.decode(plain_result, pod_result)
print(" + Result plaintext matrix ...... Correct.")
print_matrix(pod_result, row_size)