How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Differential privacy inequality for bounded measurable functions
Statement
Let and be probability measures on a measurable space. Suppose , , and for every measurable set . Then every measurable function with satisfies
Statement references
Facts & Assumptions
Given: Probability measures , , , the event inequality in the Statement, and a measurable with .
Expectation is integration against the probability measure.
Simple integrals are the finite sums for disjoint measurable and .
Monotone convergence passes increasing nonnegative functions through each integral.
Proof
For each integer , put and define . Each is a measurable simple function and . Every level counted at stage contributes the two levels and at stage , so . The largest dyadic level not exceeding any fixed differs from by less than , so .
Apply the given event inequality to the level sets in step 1.1 and use [F3] on disjoint simple representations. Partition the range into for and . Then , while each level set is the disjoint union of . Rearranging these finite sums gives . This holds for every .
Let in step 2.1. By [F4] and step 1.1, both nonnegative integrals converge to the corresponding integrals of , so .
Depends on
Used by
Dependency tree · two levels
22 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Cynthia Dwork and Aaron Roth, The Algorithmic Foundations of Differential Privacy (standard reference, not scraped)
- Salil Vadhan, The Complexity of Differential Privacy, 2017 manuscript (standard reference, not scraped)