Publication | Open Access
Optimal error of query sets under the differentially-private matrix mechanism
41
Citations
28
References
2013
Year
Unknown Venue
Mathematical ProgrammingPrivacy ProtectionEngineeringOptimal ErrorInformation SecurityInformation ForensicsComputational ComplexityMatrix TheoryData ScienceData AnonymizationManagementPrivacy EngineeringData IntegrationBig DataCombinatorial OptimizationData ManagementApproximation TheoryStatisticsFormal Privacy GuaranteeData PrivacyPrivate Information RetrievalComputer ScienceAlgorithmic Information TheoryDifferential PrivacyPrivacyData SecurityCryptographyPrivacy ResearchSynthetic DataRandom MatrixData Modeling
A common goal of privacy research is to release synthetic data that satisfies a formal privacy guarantee and can be used by an analyst in place of the original data. To achieve reasonable accuracy, a synthetic data set must be tuned to support a specified set of queries accurately, sacrificing fidelity for other queries.
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