The Algorithmic Foundations of Differential Privacy

Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 3-4.

Type

Journal article

Intellectual base

Primary research

Year

2014

Volume

9

Issue

3–4

Pages

3-4

DOI

10.1561/0400000042

DOI registrant

Emerald

Link

https://doi.org/10.1561/0400000042

Cited from

computer_science, engineering_design, mathematics, neuroscience, statistics_experimental_design

Cited by

10 citations across 10 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Blinding
  • The privacy field generalizes the cut from "remove the information" to "add calibrated noise so the information is recoverable only at known probability"

    This sourceGeneralizes the channel-cut from full removal to adding calibrated noise so a value is recoverable only at a known probability — channel attenuation with a noise wall in place of a clean cut.

• Hidden Information Reconstruction
  • Differential privacy is structurally a channel-narrowing technology; generative-noise training is a prior-denial technology; each addresses a different leg of the reconstruction, and a defence that addresses neither is theatrical.

    This sourceCanonical treatment of differential privacy as calibrated channel-narrowing, the adversary-relative privacy guarantee, and composition theorems quantifying super-additive leakage across disclosures.

• Identifiability
  • And the dual of identifiability is anonymity: the same test with the success criterion flipped, so that differential privacy, k-anonymity, and indistinguishability obfuscation specify minimum sizes of the equivalence class rather than demanding a singleton.

    This sourceTreats privacy as a minimum size of an equivalence class of neighboring databases (the dual of identifiability), with the budget ε sizing the guaranteed indistinguishability.

• Linearity
  • Listed in the references but not attached to a specific claim.
• Measurement Uncertainty and Observational Noise
  • Listed in the references but not attached to a specific claim.
• Observability
  • T3 — Privacy and observability tradeoffs
• Predictive Coding
  • Listed in the references but not attached to a specific claim.

Mechanisms

• Controlled Noise Injection
  • The discipline is to fix the noise for a fixed query and to keep a cumulative budget ledger, so total leakage stays bounded no matter how often the system is asked.

    This sourceExplains why duplicate fixed queries should reuse a coordinated noisy answer and why cumulative privacy loss must be accounted for under composition.

• Privacy Budget Accounting
  • The discipline is to fix the budget from the protection requirement before demand is known, and to treat an exhausted budget as a real stop rather than a paperwork obstacle.

    This sourceShows how target cumulative-privacy parameters determine per-mechanism privacy allowances under composition.

• Query Rate and Overlap Limit
  • The discipline is to derive the overlap and rate ceilings from the reconstruction they must prevent, and to pair them with identity assurance and cumulative accounting rather than trusting them alone.

    This sourceEstablishes cumulative privacy-loss accounting across repeated mechanisms and shows why per-query restrictions cannot be trusted in isolation.

Verification

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