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Smartnoise-sql - Opendp 0.14.1: Fix float domains that implicitly allows NaN #626

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Smartnoise-sql - Opendp 0.14.1: Fix float domains that implicitly allows NaN #626

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c79b644
bump opendp
LancelotMarti Feb 20, 2026
53b4845
Specify atom_domain must not be nan
LancelotMarti Mar 4, 2026
41db807
bump and fix missing atom domain
LancelotMarti Mar 4, 2026
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2 changes: 1 addition & 1 deletion sql/VERSION
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@@ -1 +1 @@
1.0.7
1.0.9
4 changes: 2 additions & 2 deletions sql/pyproject.toml
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@@ -1,6 +1,6 @@
[tool.poetry]
name = "smartnoise-sql"
version = "1.0.7"
version = "1.0.9"
description = "Differentially Private SQL Queries"
authors = ["SmartNoise Team <smartnoise@opendp.org>"]
license = "MIT"
Expand All @@ -11,7 +11,7 @@ readme = "README.md"

[tool.poetry.dependencies]
python = ">=3.9,<3.14"
opendp = ">=0.8.0,<0.13.0"
opendp = ">=0.8.0,<=0.14.1"
antlr4-python3-runtime = "4.9.3"
PyYAML = "^6.0.1"
graphviz = ">=0.17,<1.0"
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2 changes: 1 addition & 1 deletion sql/snsql/sql/_mechanisms/approx_bounds.py
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Expand Up @@ -57,7 +57,7 @@ def edges(idx):
enable_features('floating-point', 'contrib')
discovered_scale = 1.0 / epsilon

input_domain = dp.atom_domain(T=float)
input_domain = dp.atom_domain(T=float, nan=False)
input_metric = dp.absolute_distance(T=float)

meas = make_laplace(input_domain, input_metric, discovered_scale)
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4 changes: 2 additions & 2 deletions sql/snsql/sql/_mechanisms/gaussian.py
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Expand Up @@ -38,7 +38,7 @@ def _compute_noise_scale(self):
raise ValueError(f"Noise scale is too large using epsilon={self.epsilon} and bounds ({lower}, {upper}) with {self.mechanism}. Try preprocessing to reduce senstivity, or try different privacy parameters.")
enable_features('floating-point', 'contrib')

input_domain = dp.vector_domain(dp.atom_domain(T=float))
input_domain = dp.vector_domain(dp.atom_domain(T=float, nan=False))
input_metric = dp.symmetric_distance()

bounded_sum = (input_domain, input_metric) >> dp.t.then_clamp(bounds=bounds) >> dp.t.then_sum()
Expand Down Expand Up @@ -68,7 +68,7 @@ def release(self, vals):
enable_features('contrib')
bit_depth = self.bit_depth
set_default_int_type(f"i{bit_depth}")
meas = make_gaussian(dp.atom_domain(T=float), dp.absolute_distance(T=float), self.scale)
meas = make_gaussian(dp.atom_domain(T=float, nan=False), dp.absolute_distance(T=float), self.scale)
vals = [meas(float(v)) for v in vals]
return vals
def accuracy(self, alpha):
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4 changes: 2 additions & 2 deletions sql/snsql/sql/_mechanisms/laplace.py
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Expand Up @@ -38,7 +38,7 @@ def _compute_noise_scale(self):

enable_features('floating-point', 'contrib')

input_domain = dp.vector_domain(dp.atom_domain(T=float))
input_domain = dp.vector_domain(dp.atom_domain(T=float, nan=False))
input_metric = dp.symmetric_distance()

bounded_sum = (input_domain, input_metric) >> dp.t.then_clamp(bounds=bounds) >> dp.t.then_sum()
Expand All @@ -64,7 +64,7 @@ def threshold(self):
return thresh
def release(self, vals):
enable_features('floating-point', 'contrib')
input_domain = dp.atom_domain(T=float)
input_domain = dp.atom_domain(T=float, nan=False)
input_metric = dp.absolute_distance(T=float)
meas = make_laplace(input_domain, input_metric, self.scale)
vals = [meas(float(v)) for v in vals]
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10 changes: 5 additions & 5 deletions synth/snsynth/transform/standard.py
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Expand Up @@ -68,18 +68,18 @@ def _fit_finish(self):

enable_features("floating-point", "contrib")

input_domain = dp.vector_domain(dp.atom_domain(T=float), size=n)
input_domain = dp.vector_domain(dp.atom_domain(T=float, nan=False), size=n)
input_metric = dp.symmetric_distance()

var_pre = (input_domain, input_metric) >> dp.t.then_clamp(bounds) >> dp.t.then_variance()
mean_pre = (input_domain, input_metric) >> dp.t.then_clamp(bounds) >> dp.t.then_mean()

v_e = self.epsilon * 0.8
m_e = self.epsilon - v_e
v_s = binary_search_param(lambda s: var_pre >> make_laplace(dp.atom_domain(T=float), dp.absolute_distance(T=float), s), d_in=1, d_out=v_e, T=float)
m_s = binary_search_param(lambda s: mean_pre >> make_laplace(dp.atom_domain(T=float), dp.absolute_distance(T=float), s), d_in=1, d_out=m_e, T=float)
dpvar = var_pre >> make_laplace(dp.atom_domain(T=float), dp.absolute_distance(T=float), v_s)
dpmean = mean_pre >> make_laplace(dp.atom_domain(T=float), dp.absolute_distance(T=float), m_s)
v_s = binary_search_param(lambda s: var_pre >> make_laplace(dp.atom_domain(T=float, nan=False), dp.absolute_distance(T=float), s), d_in=1, d_out=v_e, T=float)
m_s = binary_search_param(lambda s: mean_pre >> make_laplace(dp.atom_domain(T=float, nan=False), dp.absolute_distance(T=float), s), d_in=1, d_out=m_e, T=float)
dpvar = var_pre >> make_laplace(dp.atom_domain(T=float, nan=False), dp.absolute_distance(T=float), v_s)
dpmean = mean_pre >> make_laplace(dp.atom_domain(T=float, nan=False), dp.absolute_distance(T=float), m_s)

self.var = dpvar(np.array(self._fit_vals))
self.var = np.clip(self.var, 0.001, (self.fit_upper - self.fit_lower) ** 2 / 4)
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6 changes: 3 additions & 3 deletions synth/snsynth/utils.py
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Expand Up @@ -30,7 +30,7 @@ def exponential_mechanism(qualities, epsilon, sensitivity=1.0, base_measure=None

def gaussian_noise(sigma, size=None):
enable_features('floating-point', 'contrib')
input_domain = dp.atom_domain(T=float)
input_domain = dp.atom_domain(T=float, nan=False)
input_metric = dp.absolute_distance(T=float)
meas = make_gaussian(input_domain, input_metric, sigma)
if size is None:
Expand All @@ -40,7 +40,7 @@ def gaussian_noise(sigma, size=None):

def laplace_noise(scale, size=None):
enable_features('floating-point', 'contrib')
input_domain = dp.atom_domain(T=float)
input_domain = dp.atom_domain(T=float, nan=False)
input_metric = dp.absolute_distance(T=float)
meas = make_laplace(input_domain, input_metric, scale)
if size is None:
Expand All @@ -52,7 +52,7 @@ def cdp_rho(epsilon, delta, max_contrib=1):
# return a rho that satisfies (epsilon, delta)
budget = (epsilon, delta)
enable_features('floating-point', 'contrib')
input_domain = dp.atom_domain(T=float)
input_domain = dp.atom_domain(T=float, nan=False)
input_metric = dp.absolute_distance(T=float)
def make_adp_gauss(scale):
test_gauss = make_gaussian(input_domain, input_metric, scale)
Expand Down