What is recorded here?
Formalized a strong indistinguishability-based privacy guarantee and showed how to answer numerical queries by calibrating random noise to a function’s sensitivity.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the TCC 2006 four-author paper. It does not collapse earlier privacy work or later differential-privacy composition, accounting and deployment results into this one contribution.
Read the original work
- Microsoft Research: Calibrating Noise to Sensitivity in Private Data Analysis
- Dwork, McSherry, Nissim and Smith, Calibrating Noise to Sensitivity
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Cynthia Dwork; Frank McSherry; Kobbi Nissim; Adam Smith
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States, Israel
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English catalogue narrative
Dwork, McSherry, Nissim and Smith: calibrating noise to sensitivity
Formalized a strong indistinguishability-based privacy guarantee and showed how to answer numerical queries by calibrating random noise to a function’s sensitivity.
Scientific scope
The record is the TCC 2006 four-author paper. It does not collapse earlier privacy work or later differential-privacy composition, accounting and deployment results into this one contribution.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Cynthia Dwork; Frank McSherry; Kobbi Nissim; Adam Smith
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.