コード例 #1
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def tpr_fixed_key(net, n, key, nr=7, diff=(0x40, 0x0), batch_size=5000):
    pt0a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt1a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt0b, pt1b = pt0a ^ diff[0], pt1a ^ diff[1]
    ks = sp.expand_key(key, nr)
    ct0a, ct1a = sp.encrypt((pt0a, pt1a), ks)
    ct0b, ct1b = sp.encrypt((pt0b, pt1b), ks)
    X = sp.convert_to_binary([ct0a, ct1a, ct0b, ct1b])
    Z = net.predict(X, batch_size=batch_size).flatten()
    acc = np.sum(Z > 0.5) / n
    v = 1 - Z
    mse = np.mean(v * v)
    return (acc, mse)
コード例 #2
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def make_testset(n, nr=7, diff=(0x40, 0x0)):
    Y = np.frombuffer(urandom(n), dtype=np.uint8)
    Y = Y & 1
    pt0a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt1a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt0b, pt1b = pt0a ^ diff[0], pt1a ^ diff[1]
    keys = np.frombuffer(urandom(8 * n), dtype=np.uint16).reshape(4, -1)
    ks = sp.expand_key(keys, nr)
    ct0a, ct1a = sp.encrypt((pt0a, pt1a), ks)
    ct0b, ct1b = sp.encrypt((pt0b, pt1b), ks)
    num_rnd = np.sum(Y == 0)
    ct0b[Y == 0] = np.frombuffer(urandom(2 * num_rnd), dtype=np.uint16)
    ct1b[Y == 0] = np.frombuffer(urandom(2 * num_rnd), dtype=np.uint16)
    return ([ct0a, ct1a, ct0b, ct1b], Y)
コード例 #3
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def test(n):
    pt0a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt1a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
    pt0b, pt1b = pt0a ^ 0x40, pt1a ^ 0x0
    keys = np.frombuffer(urandom(8 * n), dtype=np.uint16).reshape(4, -1)
    ks = sp.expand_key(keys, 7)
    ct0a, ct1a = sp.encrypt((pt0a, pt1a), ks)
    ct0b, ct1b = sp.encrypt((pt0b, pt1b), ks)
    Z1 = evaluate_ciphertexts([ct0a, ct1a, ct0b, ct1b])
    r = np.frombuffer(urandom(8 * n), dtype=np.uint16).reshape(4, -1)
    Z0 = evaluate_ciphertexts(r)
    tpr, tnr = np.sum(Z1 > 0.5), np.sum(Z0 < 0.5)
    tpr = tpr / n
    tnr = tnr / n
    acc = (tpr + tnr) / 2
    print("Acc: ", acc, ", TPR: ", tpr, ", TNR: ", tnr)
コード例 #4
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def wrong_key_decryption(n, diff=(0x0040, 0x0), nr=7, net=net7):
    means = np.zeros(2**16)
    sig = np.zeros(2**16)
    for i in range(2**16):
        keys = np.frombuffer(urandom(8 * n), dtype=np.uint16).reshape(4, -1)
        ks = sp.expand_key(keys, nr + 1)
        #ks[nr-1] = 17123;
        pt0a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
        pt1a = np.frombuffer(urandom(2 * n), dtype=np.uint16)
        pt0b, pt1b = pt0a ^ diff[0], pt1a ^ diff[1]
        ct0a, ct1a = sp.encrypt((pt0a, pt1a), ks)
        ct0b, ct1b = sp.encrypt((pt0b, pt1b), ks)
        rsubkeys = i ^ ks[nr]
        #rsubkeys = rdiff ^ 0;
        c0a, c1a = sp.dec_one_round((ct0a, ct1a), rsubkeys)
        c0b, c1b = sp.dec_one_round((ct0b, ct1b), rsubkeys)
        X = sp.convert_to_binary([c0a, c1a, c0b, c1b])
        Z = net.predict(X, batch_size=10000)
        Z = Z.flatten()
        means[i] = np.mean(Z)
        sig[i] = np.std(Z)
    return (means, sig)
コード例 #5
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def key_rank_one_round(nr, net, n_blocks=1, diff=(0x0040, 0x0)):
    pt0a = np.frombuffer(urandom(2 * n_blocks),
                         dtype=np.uint16).reshape(n_blocks, -1)
    pt1a = np.frombuffer(urandom(2 * n_blocks),
                         dtype=np.uint16).reshape(n_blocks, -1)
    pt0b, pt1b = pt0a ^ diff[0], pt1a ^ diff[1]
    pt0a, pt1a = sp.dec_one_round((pt0a, pt1a), 0)
    pt0b, pt1b = sp.dec_one_round((pt0b, pt1b), 0)
    key = np.frombuffer(urandom(8), dtype=np.uint16)
    ks = sp.expand_key(key, nr)
    k1 = ks[nr - 1]
    ct0a, ct1a = sp.encrypt((pt0a, pt1a), ks)
    ct0b, ct1b = sp.encrypt((pt0b, pt1b), ks)
    trial_keys = np.arange(2**16)
    c0a, c1a = sp.dec_one_round((ct0a, ct1a), trial_keys)
    c0b, c1b = sp.dec_one_round((ct0b, ct1b), trial_keys)
    c1a = np.tile(c1a, 2**16)
    c1b = np.tile(c1b, 2**16)
    #the next two lines are the only bits of this function that change
    #if instead of a neural network the difference distribution table is used for inference
    #in particular, in this case, conversion to network input is replaced by calculation of trial decryption differences
    #Z is then calculated simply by looking up the relevant transition probabilities in the ddt
    #instead of a neural net, the function then expects as second input a table of size 2**32
    X = sp.convert_to_binary(
        [c0a.flatten(),
         c1a.flatten(),
         c0b.flatten(),
         c1b.flatten()])
    Z = net.predict(X, batch_size=10000)
    Z = Z / (1 - Z)
    Z = np.log2(Z)
    Z = Z.reshape(n_blocks, -1)
    Z = np.sum(Z, axis=0)
    rank0 = np.sum(Z > Z[k1])
    rank1 = np.sum(Z >= Z[k1])
    return (rank0, rank1)
コード例 #6
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def gen_key(nr):
    key = np.frombuffer(urandom(8), dtype=np.uint16)
    ks = sp.expand_key(key, nr)
    return (ks)
コード例 #7
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                diff = (randint(0, (2**16) - 1), randint(0, (2**16) - 1))
            elif (sys.argv[11] == "fix_diff"):
                diff = (int(sys.argv[12], 16), int(sys.argv[13], 16))
            if (loop == 1):
                keys = np.repeat(np.frombuffer(urandom(8),
                                               dtype=np.uint16).reshape(4, -1),
                                 n,
                                 axis=1)

            plain0_0 = np.frombuffer(urandom(2 * n), dtype=np.uint16)
            plain0_1 = np.frombuffer(urandom(2 * n), dtype=np.uint16)

            plain1_0 = plain0_0 ^ diff[0]
            plain1_1 = plain0_1 ^ diff[1]

            ks = sp.expand_key(keys, (r_start - 1) + nr)
            cdata0 = sp.encrypt((plain0_0, plain0_1), ks, r_start)
            cdata1 = sp.encrypt((plain1_0, plain1_1), ks, r_start)
            cdata0_mid = sp.encrypt((plain0_0, plain0_1), ks[0:r_mid], r_start)
            cdata1_mid = sp.encrypt((plain1_0, plain1_1), ks[0:r_mid], r_start)
            X = convert_to_binary_64_block(np.array(cdata0 ^ cdata1), 16, 2)

        elif (sys.argv[1] == "simon"):
            diff_default = (int(file_name.split('_')[4][1:-1].split(',')[0]),
                            int(file_name.split('_')[4][1:-1].split(',')[1]))
            if (sys.argv[11] == "default_diff"):
                diff = diff_default
            elif (sys.argv[11] == "random_diff" and loop == 1):
                diff = (randint(0, (2**16) - 1), randint(0, (2**16) - 1))
            elif (sys.argv[11] == "fix_diff"):
                diff = (int(sys.argv[12], 16), int(sys.argv[13], 16))