- Second vignette
bernsteinon: Bernstein approximations and use ofDPMechBernsteinfor private function release. - Minor edits to docs
- Expanding test coverage of Bernstein mechanism and function approximation code.
- Addition of
S3constructor andpredict()generic implementation for fitting (non-iterated) Bernstein polynomial function approximations. - Addition of
DPMechBernsteinclass implementing the Bernstein mechanism of Alda and Rubinstein (AAAI'2017), for privately releasing functions. - Bug fix in the Laplace random sampler affecting
DPMechLaplace - Unit test coverage of new functionality; general documentation improvements.
- Addition of
DPMechGaussianclass for the generic Gaussian mechanism to README, Vignette. Resolves #2 - Minor test additions.
- Refactoring around
releaseResponse()method inDPMechNumeric. Resolves #1 - Increased test coverage.
- New
DPMechGaussianclass implementing the Gaussian mechanism, which achieves (epsilon,delta)-differential privacy by adding Gaussian noise to numeric responses calibrated by L2-norm sensitivity. - Refactoring of
DPMechGaussianandDPMechLaplaceunderneath a newVIRTUALclassDPMechNumericwhich contains common methods,dimsslot (formerlydimchanged becausedimis a special slot for S4).
DPMechLaplaceobjects can now be initialized without specifying non-privatetargetresponsedim. In such cases, the sensitivity sampler will perform an additionaltargetprobe to determinedim.
- Sensitivity sampler methods no longer require oracles that return lists. Acceptable oracles may now return lists, matrices, data frames, numeric vectors, or char vectors. As a consequence some example code in docs, README and vignette, is simplified.
- Initial release