SIS Fixes

estimator/sis.py

@@ -8,14 +8,15 @@
 
 from .sis_lattice import lattice
 from .sis_parameters import SISParameters as Parameters  # noqa
-from .conf import (red_cost_model as red_cost_model_default,
-                   red_shape_model as red_shape_model_default)
+from .conf import (
+    red_cost_model as red_cost_model_default,
+    red_shape_model as red_shape_model_default,
+)
 from .util import batch_estimate, f_name
 from .reduction import RC
 
 
 class Estimate:
-
     def rough(self, params, jobs=1, catch_exceptions=True):
         """
         This function makes the following somewhat routine assumptions:
@@ -47,7 +48,8 @@ def rough(self, params, jobs=1, catch_exceptions=True):
         )
         res_raw = res_raw[params]
         res = {
-            algorithm: v for algorithm, attack in algorithms.items()
+            algorithm: v
+            for algorithm, attack in algorithms.items()
             for k, v in res_raw.items()
             if f_name(attack) == k
         }
@@ -91,8 +93,8 @@ def __call__(
             >>> _ = SIS.estimate(params)
             lattice              :: rop: ≈2^47.0, red: ≈2^47.0, δ: 1.011391, β: 61, d: 276, tag: euclidean
 
-            >>> _ = SIS.estimate(params.updated(norm=oo), red_shape_model="cn11")
-            lattice              :: rop: ≈2^43.6, red: ≈2^42.6, sieve: ≈2^42.7, β: 40, η: 67, ζ: 112, d: 750, ...
+            >>> _ = SIS.estimate(params.updated(length_bound=16, norm=oo), red_shape_model="cn11")
+            lattice              :: rop: ≈2^65.9, red: ≈2^64.9, sieve: ≈2^64.9, β: 113, η: 142, ζ: 0, d: 2486, ...
         """
 
         algorithms = {}

estimator/sis_lattice.py

@@ -26,12 +26,13 @@ class SISLattice:
     """
     Estimate cost of solving SIS via lattice reduction.
     """
+
     @staticmethod
     def _solve_for_delta_euclidean(params, d):
         # root_volume = params.q**(params.n/d)
         # delta = (params.length_bound / root_volume)**(1/(d - 1))
-        root_volume = (params.n/d) * log(params.q, 2)
-        log_delta = (1/(d - 1)) * (log(params.length_bound, 2) - root_volume)
+        root_volume = (params.n / d) * log(params.q, 2)
+        log_delta = (1 / (d - 1)) * (log(params.length_bound, 2) - root_volume)
         return RR(2**log_delta)
 
     @staticmethod
@@ -40,7 +41,7 @@ def _opt_sis_d(params):
         Optimizes SIS dimension for the given parameters, assuming the optimal
         d \approx sqrt(n*log(q)/log(delta))
         """
-        log_delta = log(params.length_bound, 2)**2 / (4 * params.n * log(params.q, 2))
+        log_delta = log(params.length_bound, 2) ** 2 / (4 * params.n * log(params.q, 2))
         d = sqrt(params.n * log(params.q, 2) / log_delta)
         return d
 
@@ -71,8 +72,10 @@ def cost_euclidean(
             beta = d
             reduction_possible = False
 
-        lb = min(RR(sqrt(params.n * log(params.q))), RR(sqrt(d) * params.q**(params.n/d)))
-        return costf(red_cost_model, beta, d, predicate=params.length_bound > lb and reduction_possible)
+        lb = min(RR(sqrt(params.n * log(params.q))), RR(sqrt(d) * params.q ** (params.n / d)))
+        return costf(
+            red_cost_model, beta, d, predicate=params.length_bound > lb and reduction_possible
+        )
 
     @staticmethod
     @cached_function
@@ -110,6 +113,9 @@ def cost_infinity(
         # Calculate the basis shape to aid in both styles of analysis
         d_ = d - zeta
 
+        if d_ < beta:
+            return Cost(rop=oo, mem=oo)
+
         r = simulator(d=d_, n=d_ - params.n, q=params.q, beta=beta, xi=1, tau=False)
 
         # Cost the sampling of short vectors.
@@ -121,7 +127,7 @@ def cost_infinity(
             vector_length = rho * sqrt(r[0])
             # Find probability that all coordinates meet norm bound
             sigma = vector_length / sqrt(d_)
-            log_trial_prob = RR(d_*log(1 - 2*gaussian_cdf(0, sigma, -params.length_bound), 2))
+            log_trial_prob = RR(d_ * log(1 - 2 * gaussian_cdf(0, sigma, -params.length_bound), 2))
 
         else:  # Dilithium style analysis
             # Find first non-q-vector in r
@@ -142,10 +148,14 @@ def cost_infinity(
             gaussian_coords = max(idx_end - idx_start + 1, sieve_dim)
             sigma = vector_length / sqrt(gaussian_coords)
 
-            log_trial_prob = RR(log(1 - 2*gaussian_cdf(0, sigma, -params.length_bound), 2)*(gaussian_coords))
-            log_trial_prob += RR(log((2*params.length_bound + 1)/params.q, 2)*(idx_start))
+            log_trial_prob = RR(
+                log(1 - 2 * gaussian_cdf(0, sigma, -params.length_bound), 2) * (gaussian_coords)
+            )
+            log_trial_prob += RR(log((2 * params.length_bound + 1) / params.q, 2) * (idx_start))
 
-        probability = 2**min(0, log_trial_prob + RR(log(N, 2)))  # expected number of solutions (max 1)
+        probability = 2 ** min(
+            0, log_trial_prob + RR(log(N, 2))
+        )  # expected number of solutions (max 1)
         ret = Cost()
         ret["rop"] = cost_red
         ret["red"] = bkz_cost["rop"]
@@ -272,11 +282,11 @@ def __call__(
             >>> SIS.lattice(params)
             rop: ≈2^47.0, red: ≈2^47.0, δ: 1.011391, β: 61, d: 276, tag: euclidean
 
-            >>> SIS.lattice(params.updated(norm=oo), red_shape_model="lgsa")
-            rop: ≈2^43.6, red: ≈2^42.6, sieve: ≈2^42.7, β: 40, η: 67, ζ: 112, d: 750, prob: 1, ↻: 1, tag: infinity
+            >>> SIS.lattice(params.updated(norm=oo, length_bound=16), red_shape_model="lgsa")
+            rop: ≈2^61.0, red: ≈2^59.9, sieve: ≈2^60.1, β: 95, η: 126, ζ: 0, d: 2486, prob: 1, ↻: 1, tag: infinity
 
-            >>> SIS.lattice(params.updated(norm=oo), red_shape_model="cn11")
-            rop: ≈2^43.6, red: ≈2^42.6, sieve: ≈2^42.7, β: 40, η: 67, ζ: 112, d: 750, prob: 1, ↻: 1, tag: infinity
+            >>> SIS.lattice(params.updated(norm=oo, length_bound=16), red_shape_model="cn11")
+            rop: ≈2^65.9, red: ≈2^64.9, sieve: ≈2^64.9, β: 113, η: 142, ζ: 0, d: 2486, prob: 1, ↻: 1, tag: infinity
 
         The success condition for euclidean norm bound is derived by determining the root hermite factor required for
         BKZ to produce the required output. For infinity norm bounds, the success conditions are derived using a
@@ -291,7 +301,9 @@ def __call__(
         elif params.norm == oo:
             tag = "infinity"
         else:
-            raise NotImplementedError("SIS attack estimation currently only supports euclidean and infinity norms")
+            raise NotImplementedError(
+                "SIS attack estimation currently only supports euclidean and infinity norms"
+            )
 
         if tag == "infinity":
             red_shape_model = simulator_normalize(red_shape_model)
@@ -305,7 +317,7 @@ def __call__(
             )
 
             if zeta is None:
-                with local_minimum(0, params.n, log_level=log_level) as it:
+                with local_minimum(0, params.m, log_level=log_level) as it:
                     for zeta in it:
                         it.update(
                             f(

estimator/sis_parameters.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from dataclasses import dataclass
 
-from sage.all import log, ceil
+from sage.all import log, ceil, oo
 
 
 @dataclass
@@ -19,7 +19,11 @@ class SISParameters:
 
     def __post_init__(self, **kwds):
         if not self.m:
-            self.m = ceil(self.n*log(self.q))  #: Set m to be the minimum required for a solution to exist.
+            #: Set m to be the minimum required for a solution to exist.
+            if self.norm == oo:
+                self.m = 2 * ceil(self.n * log(self.q, (2 * self.length_bound + 1)))
+            else:
+                self.m = 2 * ceil(self.n * log(self.q, 2))
 
     @property
     def _homogeneous(self):