Shoeboxam

import opendp.prelude as dp

model = dp.sklearn.PCA(
    epsilon=1.,
    row_norm=1.,
    n_samples=num_rows,
    n_features=4,
)

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import faker
import opendp.prelude as dp

counter = dp.t.make_count_by(
    dp.vector_domain(dp.atom_domain(T=str)),
    dp.symmetric_distance(),
    MO=dp.L1Distance[int])

alp_meas = counter >> dp.m.then_alp_queryable(
    scale=1.,

Shoeboxam

import opendp.prelude as dp
import pandas as pd
import faker
import random

# first, write constructors that will be used to build the mechanism


def make_grouping_cols_score(candidates, min_bin_contributions):
    r"""Create a transformation that assesses the utility of each candidate in `candidates`.

Shoeboxam

import opendp.prelude as dp

dp.enable_features("contrib")

# define privacy guarantee
max_contributions = 1
epsilon = 0.1

# public information
candidates = [10, 30, 50, 70, 90]

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def bench(function, iterations):
    import time
    elapsed_times = []
    
    import tracemalloc
    tracemalloc.start()

    for _ in range(iterations):
        prev_snap = tracemalloc.take_snapshot()
        prev_time = time.time()

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from dataclasses import dataclass
from typing import Callable, Any


@dataclass
class MockMeasurement(object):
    input_domain: Any
    output_domain: Any
    function: Callable
    input_metric: Any

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# Pseudocode of an OpenDP combinator for privatizing a certain class of vector-valued transformations

def make_some_transformation_stable(trans_query: Transformation, scale, threshold):
    assert trans_query.input_metric == SymmetricDistance
    assert trans_query.output_metric == L1Distance
    # must be equivalent to representation 2 in https://arxiv.org/pdf/1709.05396.pdf, Section 2.2.1
    assert trans_query.output_domain == HashMapDomain[AllDomain[KeyType], AllDomain[FloatCountType]]

    def function(data):
        noised = {k: v + sample_laplace(scale) for k, v in trans_query.invoke(data)}

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import pandas as pd
import matplotlib.pyplot as plt
from dateutil import parser
plt.style.use("bmh")

data = pd.read_excel("/Users/michael/Downloads/MD 4 year.XLS", parse_dates=['Gift Date'])


def plot_cumulative_per_year(event_days, min_delta, max_delta):
    cumulative_sums = []

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WhiteNoise Core
Differential Privacy Library Python Bindings

This repository contains python bindings to Whitenoise-Core and its underlying Rust binaries.

• For examples of this library in action, please see the Python notebooks in WhiteNoise-Samples.
• In addition, see the accompanying WhiteNoise-System repository which includes WhiteNoise tools for differential privacy.

---

Shoeboxam

import d3m
from d3m.metadata.pipeline import Pipeline, PrimitiveStep
from d3m.metadata.base import ArgumentType

from google.protobuf.json_format import MessageToJson
from ta3ta2_api import utils
import json

types = [
    utils.ValueType.RAW,