Add a tool for plotting Prio3 robustness bounds

poc/plot_prio3_multiproof_robustness.py

@@ -0,0 +1,101 @@
+# prio3_multiproof_robustness.py - Plot robustness bounds for various parameters.
+# Use `sage -python prio3_multiproof_robustness.py`
+import math
+
+import matplotlib.pyplot as plt
+
+from field import Field64, Field128
+from vdaf_prio3 import Prio3SumVec
+
+NUM_REPORTS = 1000000000
+
+
+def soundness(gadget_calls, gadget_degree, field_size):
+    '''
+    ia.cr/2019/188, Theorem 4.3
+
+    gadget_calls - number of times the gadget is called
+
+    gadget_degree - arithmetic degree of the gadget
+
+    field_size - size of the field
+    '''
+    return gadget_calls * gadget_degree / (field_size - gadget_calls)
+
+
+def robustness(epsilon, ro_queries, prep_queries, num_proofs, seed_bits):
+    '''
+    ia.cr/2023/130, Theorem 1, assuming the bound can be modified by raising
+    `epsilon` to the power of the number of FLPs.
+
+    epsilon - soundness of the base FLP
+
+    ro_queries - random oracle queries, a proxy for the amount of precomputation
+                 done by the adversary
+
+    prep_queries - number of online attempts, a proxy for the batch size
+
+    num_proofs - number of FLPs
+
+    seed_bits - the size of the XOF seed in bits
+    '''
+    return (ro_queries + prep_queries) * epsilon**num_proofs + \
+           (ro_queries + prep_queries**2) / 2**(seed_bits - 1)
+
+
+def sum_vec(field_size, num_proofs, length):
+    '''
+    Prio3SumVec (draft-irtf-cfrg-vdaf-08, Section 7.4.3): Probability of
+    accepting one report in a batch of NUM_REPORTS. Assuming the asymptotically
+    optimal chunk length.
+    '''
+    bits = 1
+    chunk_length = max(1, length**(1/2))
+    vdaf = Prio3SumVec.with_params(length, bits, chunk_length)
+    gadget_calls = vdaf.Flp.Valid.GADGET_CALLS[0]
+    gadget_degree = vdaf.Flp.Valid.GADGETS[0].DEGREE
+
+    base_flp_soundness = soundness(gadget_calls, gadget_degree, field_size)
+
+    # SumVec interprets the inner Mul-gadget outputs as coefficients of a
+    # polynomial and evaluates the polynomial at a random point. If a gadget
+    # output is non-zero, then the output is non-zero except with this
+    # probability. This is bounded by the number of roots of the polynomial.
+    circuit_soundness = length * bits / field_size
+
+    return robustness(
+        base_flp_soundness + circuit_soundness,  # ia.cr/2019/188, Theorem 5.3
+        2**80,
+        NUM_REPORTS,
+        num_proofs,
+        vdaf.Xof.SEED_SIZE * 8,
+    )
+
+
+print(math.log2(sum_vec(Field128.MODULUS, 1, 100000)))
+
+lengths = range(100, 10**6, 100)
+plt.plot(
+    lengths,
+    [sum_vec(Field128.MODULUS, 1, length) for length in lengths],
+    label='Field128/1',
+)
+plt.plot(
+    lengths,
+    [sum_vec(Field64.MODULUS, 2, length) for length in lengths],
+    label='Field64/2',
+)
+plt.plot(
+    lengths,
+    [sum_vec(Field64.MODULUS, 3, length) for length in lengths],
+    label='Field64/3',
+)
+
+plt.xscale('log', base=10)
+plt.yscale('log', base=2)
+plt.xlabel('Length')
+plt.ylabel('Prob(1 in {} accepted reports being invalid)'.format(NUM_REPORTS))
+plt.title('Prio3SumvVec (field/number of proofs)')
+plt.legend()
+plt.grid()
+plt.show()