A python library for the fully homomorphic encryption scheme ACES
This package proposes an implementation for the ACES cryptosystem where we take parameters
The name "PyACES" is pronounced "pisces," echoing a symbol synonymous with malleability. In the Pisces symbol, the two fishes represent a harmonious interplay between opposite directions, reflecting the dual nature of encryption and decryption.
Warning
Despite the announcement made in the research paper, this implementation has been generalized to any value
Install the package using pip:
pip install pyacesThen use the following import in your Python code.
import pyacesTo upgrade to the latest version:
pip install --upgrade pyaces==version_numberACES depends on the generation of an arithmetic channel, which is a quadruple
A brief review of section 5.1 of the research paper already informs us that the condition
$p^2 < q$ -
$p$ and$q$ coprime -
$q \gg (K_0Np)^{2^{K+1}}$ for some constant$K_0$ to process at least$K$ layers of operations $n = \mathsf{deg}(u) > 4$
For simplicity, we recommend that users use the formula below, where
Note
In addition to exploring the parameterization space of ACES, the guide elucidates specific technical aspects pertaining to the security of ACES. In particular, it gives useful recommendations on how to use ciphertexts in a secure way (for example see important considerations).
To create an arithmetic channel ArithChannel. The following snippet shows how to generate an arithmetic channel with
-
$p=2^5 = 32$ , -
$q= p^{2^2+1}+1 = 33554433$ , $\mathsf{deg}(u) = 10 = n$ - and
$N=2$
>>> from pyaces import *
>>> ac = ArithChannel(32,32**5+1,10,2)To generate the public key of a scheme with fully homomorphic properties, use the following instance:
>>> (f0,f1,vanmod,intmod,dim,N,u,tensor) = ac.publish(fhe = True)We can quickly verify that the condition
>>> u
[1]^10+[15561488]^9+[10729359]^8+[5895107]^7+[10512515]^6+[13114310]^5+[31946593]^4+[17963261]^3+[-72168201]^2 (33554433)
>>> sum(u.coefs)-intmod
0Below, we will access the private key ac.x. It is important to note that the integers ac must satisfy the relationships ArithChannel will generate a new value for
As elaborated in the research paper and the preceding section, the homomorphism property imposes an upper limit on the maximum achievable level. This limit is determined by the ratio
>>> q_p = intmod/vanmod
>>> q_p
1048576.03125To encrypt messages ACES as shown below.
>>> bob = ACES(f0,f1,vanmod,intmod,dim,N,u)The following example illustrates an encryption of the message
>>> enc3, k3 = bob.encrypt(3)
>>> k3
[2, 14]
>>> for d in enc3.dec:
... d
...
[21379560]^9+[25028370]^8+[8694508]^7+[28102446]^6+[5172890]^5+[24589603]^4+[5543292]^3+[11274872]^2+[13629468]^1+[19271064]^0 (33554433)
[9108842]^9+[5039426]^8+[21244355]^7+[3175448]^6+[22655005]^5+[15962061]^4+[20588907]^3+[26006670]^2+[27534017]^1+[16266756]^0 (33554433)
[1458536]^9+[24643021]^8+[15203777]^7+[23778795]^6+[24103882]^5+[23940451]^4+[3773861]^3+[9206299]^2+[10862051]^1+[7246808]^0 (33554433)
[22153519]^9+[7780220]^8+[16360279]^7+[28545698]^6+[22573294]^5+[163643]^4+[27158635]^3+[8665100]^2+[5091255]^1+[4834865]^0 (33554433)
[12109157]^9+[4473554]^8+[24217584]^7+[17701710]^6+[1472804]^5+[8994119]^4+[30242395]^3+[3669714]^2+[11512159]^1+[4809361]^0 (33554433)
[16571218]^9+[31797286]^8+[21517446]^7+[9449064]^6+[31547175]^5+[19754057]^4+[22483530]^3+[22371627]^2+[6812861]^1+[20406855]^0 (33554433)
[6535938]^9+[20943309]^8+[10663544]^7+[9754641]^6+[6342352]^5+[25438883]^4+[29421614]^3+[30503775]^2+[14529486]^1+[456676]^0 (33554433)
[17806160]^9+[19626456]^8+[29126106]^7+[24033050]^6+[23506780]^5+[28391799]^4+[4000439]^3+[10046721]^2+[19523779]^1+[17246181]^0 (33554433)
[15096919]^9+[15509580]^8+[14202625]^7+[28337590]^6+[9646278]^5+[31062288]^4+[26781822]^3+[8778106]^2+[20152751]^1+[5062739]^0 (33554433)
[2363338]^9+[16535237]^8+[30285612]^7+[4934198]^6+[21283726]^5+[5267871]^4+[26811682]^3+[5040335]^2+[7297640]^1+[24881255]^0 (33554433)
>>> enc3.uplvl
64In the previous example, the vector k3 can be used to determine the level of the ciphertext k3 with the vector ac.lvl_e. The integer enc3.uplvl is known to all parties and represents an upper bound on the level of k3 (and the vector ac.lvl_e) should remain confidential, and only the theoretical upper bound enc3.uplvl is deemed safe to share. In fact, it is strongly recommended to retain only integers k3[i] and securely erase k3 from the computer memory.
To decrypt an encrypted message, use the ACESReader class. The following example demonstrates how to decrypt the ciphertext enc3. As expected, we retrieve the message
>>> alice = ACESReader(ac)
>>> alice.decrypt(enc3)
3To do arithmetic on encrypted information, use the class ACESAlgebra. The following example shows how to recover the homomorphism properties:
>>> alg = ACESAlgebra(vanmod,intmod,dim,u,tensor)
>>> enc5, k5 = bob.encrypt(5)
>>> alice.decrypt(alg.add(enc3,enc5))
8
>>> alice.decrypt(alg.mult(enc3,enc5))
15Now that we have explored the features of the leveled FHE underlying ACES, let us illustrate the FHE protocol described in section 5.3 of the research paper. To make this tutorial relevant to illustrating the refreshing step for the FHE protocol, we will now take the parameters
-
$p=2^5 = 32$ , -
$q=10\cdot p^{2^2+1}+1 = 335544321$ , $\mathsf{deg}(u) = 10 = n$ - and
$N=5$
>>> from pyaces import *
>>> ac = ArithChannel(32,10*32**5+1,10,5)
>>> (f0,f1,vanmod,intmod,dim,N,u,tensor) = ac.publish(fhe = True)
>>> bob = ACES(f0,f1,vanmod,intmod,dim,N,u)
>>> alice = ACESReader(ac)
>>> alg = ACESAlgebra(vanmod,intmod,dim,u,tensor)
>>> q_p = intmod/vanmodNow, we want to consider a list array of data and its encryption through ACES. We can illustrate this with the following lines of code.
>>> import random as rd
>>> array = [rd.randint(0,5) for _ in range(8)]
>>> enc_array = [bob.encrypt(a) for a in array]As seen earlier, the algorithm bob.encrypt() returns the ciphertext and its associated level. Since we cannot share levels, we want to separate them as follows.
>>> send_array, keep_array = map(list,zip(*enc_array))We now have the following data:
>>> array
[5, 4, 5, 0, 3, 3, 1, 1]
>>> send_array
[<pyaces.aces.ACESCipher object at 0x7f7c63e9ff10>, <pyaces.aces.ACESCipher object at 0x7f7c8cecb610>, <pyaces.aces.ACESCipher object at 0x7f7c63ebd700>, <pyaces.aces.ACESCipher object at 0x7f7c63ebd070>, <pyaces.aces.ACESCipher object at 0x7f7c63ebdee0>, <pyaces.aces.ACESCipher object at 0x7f7c63ebdcd0>, <pyaces.aces.ACESCipher object at 0x7f7c63ebdc40>, <pyaces.aces.ACESCipher object at 0x7f7c63ec5f10>]
>>> keep_array
[[21, 8, 17, 14, 7], [13, 27, 18, 3, 31], [0, 3, 23, 4, 23], [16, 30, 6, 9, 13], [29, 28, 18, 18, 26], [14, 19, 3, 13, 22], [18, 1, 6, 23, 18], [20, 10, 6, 30, 0]]Since operations on levels need to be dissociated form the algebraic operations done on ciphertext, we want to initiate the refresher algebra as follows.
>>> rfr = ACESRefresher(ac)To simulate an FHE protocol, let us suppose that send_array on a remote server and consider a scenario where an algoritm of additions and multiplications as shown below runs on the encrypted data.
We define this algorithm on the non-encrypted data array (as a sanity check), on the list of encrypted data send_array and on the list of levels keep_array as instructed in section 5.3 of the research paper.
>>> true_fun = Algebra().compile("(0*1+2*3+4*5)*6+7")
>>> send_fun = alg.compile("(0*1+2*3+4*5)*6+7")
>>> keep_fun = rfr.compile("(0*1+2*3+4*5)*6+7")
...First, let us see what the algorithm is meant to output for the values in array:
>>> truth = true_fun(array)
>>> truth % 32
30Now, let us shift our focus to the server side. To recap, we previously set ACESAlgebra can likely accommodate only a single layer of a sum of multiplications. However, the send_fun function incorporates two such layers. As a result, this configuration is sufficient to surpass the levels beyond
>>> online = send_fun(send_array)
>>> online.uplvl
12582912160
>>> q_p - online.uplvl
-12572426399.96875
>>> alice.decrypt(online)
25As explained in Section 5.3 of the research paper, it is necessary to decompose the algorithm
>>> true_fun1 = Algebra().compile("0*1+2*3+4*5")
>>> send_fun1 = alg.compile("0*1+2*3+4*5")
>>> keep_fun1 = rfr.compile("0*1+2*3+4*5")When we apply
>>> truth1 = true_fun1(array)
>>> truth1 % 32
29
>>> online1 = send_fun1(send_array)
>>> online1.uplvl
2457600
>>> q_p - online1.uplvl
8028160.03125
>>> alice.decrypt(online1)
29Subsequently, we can calculate the equivalent of keep_fun1. In practice, the resulting output level k1 (or in fact any positive integer less than k1) is transmitted to the server, while maintaining the secrecy of the levels in keep_array. Then, the server uses the integer k1 to refresh the ciphertext online1.
>>> k1 = keep_fun1(rfr.process(keep_array))
>>> c1 = alg.refresh(online1,k1)
>>> c1.uplvl
2380640
>>> alice.decrypt(c1)
29As can be seen above, the refresh operation does not impact the message associated with the ciphertext; rather, it diminishes the upper bound level c1.uplvl. While this reduction may appear trivial when compared to the earlier value of online1.uplvl, the refresh operation nevertheless remains efficient in revitalizing the ciphertext.
To confirm the success of this refresh operation, we can now proceed to compute the second layer of the algorithm
>>> true_fun2 = Algebra().compile("8*6+7")
>>> send_fun2 = alg.compile("8*6+7")
>>> keep_fun2 = rfr.compile("8*6+7")If we now combine this second layer with the output of the refresh operation, we can validate that the computed ciphertext accurately recovers the true value of the computation.
>>> truth2 = true_fun2(array+[truth1])
>>> truth2 % 32
30
>>> online2 = send_fun2(send_array + [c1])
>>> online2.uplvl
12188876960
>>> q_p - online2.uplvl
-12178391199.96875
>>> alice.decrypt(online2)
30A limitation in our presentation was that our algorithm struggled to handle numerous layers of additions and multiplications. Although this might restrict the volume of information processed simultaneously, we could enhance the level range by setting