- Second vignette
bernstein
on: Bernstein approximations and use ofDPMechBernstein
for private function release. - Minor edits to docs
- Expanding test coverage of Bernstein mechanism and function approximation code.
- Addition of
S3
constructor andpredict()
generic implementation for fitting (non-iterated) Bernstein polynomial function approximations. - Addition of
DPMechBernstein
class 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
DPMechGaussian
class for the generic Gaussian mechanism to README, Vignette. Resolves #2 - Minor test additions.
- Refactoring around
releaseResponse()
method inDPMechNumeric
. Resolves #1 - Increased test coverage.
- New
DPMechGaussian
class implementing the Gaussian mechanism, which achieves (epsilon,delta)-differential privacy by adding Gaussian noise to numeric responses calibrated by L2-norm sensitivity. - Refactoring of
DPMechGaussian
andDPMechLaplace
underneath a newVIRTUAL
classDPMechNumeric
which contains common methods,dims
slot (formerlydim
changed becausedim
is a special slot for S4).
DPMechLaplace
objects can now be initialized without specifying non-privatetarget
responsedim
. In such cases, the sensitivity sampler will perform an additionaltarget
probe 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