Calibrating Noise to Sensitivity in Private Data Analysis¶
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating Noise to Sensitivity in Private Data Analysis. Theory of Cryptography Conference (TCC).
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Identifiability
- The privacy mechanism adds calibrated random noise so that the induced equivalence class — the set of databases that could have produced the observed output — is large and, crucially, contains both the database with the individual and the one without.
This sourceFounds differential privacy: calibrated noise makes neighboring databases (with and without an individual) nearly indistinguishable, enlarging the equivalence class.
- The privacy mechanism adds calibrated random noise so that the induced equivalence class — the set of databases that could have produced the observed output — is large and, crucially, contains both the database with the individual and the one without.
Mechanisms¶
- Noise or Randomization Release
- The discipline is to fix the protection budget before the data is seen and let the noise follow from it
This sourceDerives the noise distribution from the chosen privacy parameter and the query's sensitivity.
- The discipline is to fix the protection budget before the data is seen and let the noise follow from it
Verification¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:f4b426b57e5e · see in the full table