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DP Lib 1.1.0
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@miracvbasaran miracvbasaran released this 02 Aug 12:49
· 110 commits to main since this release
v1.1.0
8d1c557

This is a minor release with new features, improvements and bug fixes across the libraries. There should be no breaking changes.

Overview Table

Algorithm C++ Go Java
Laplace mechanism ✅ ✅ ✅
Gaussian mechanism ✅ ✅ ✅
Laplace mechanism ✅ ✅ ✅
Count ✅ ✅ ✅
Sum ✅ ✅ ✅
Mean ✅ ✅ ✅
Variance ✅ ✅ ❌
Standard deviation ✅ ✅ ❌
Quantiles ✅ ✅ ✅
Automatic bounds approximation ✅ ❌ ❌
Truncated geometric thresholding ✅ ✅ ✅
Laplace thresholding ✅ ✅ ✅
Gaussian thresholding ✅ ✅ ✅

✅ => supported ; ❌ => not supported yet


New features since the 1.0.0 release

C++

  • Support for Gaussian Partition Selection
  • NumericalMechanism supports GetVariance
  • Users can have the library automatically select the numerical mechanism (Laplace or Gaussian) with the smaller variance

Java

  • Confidence intervals for Quantiles

Go

  • Support for variance and standard deviation

Privacy on Beam

  • Support for multiple quantiles using quantile trees

Bug Fixes

Privacy on Beam

  • Fix a privacy bug in DistinctPerKey where contributions might not be bound correctly in some rare cases
  • Fix a bug in codelab in sum.go and multiple.go where instead of summing up revenue we sum up time spent

Other

Privacy on Beam

  • Refactor error reporting, errors are propagated up to top-level functions as much as possible

Usage

Java via Maven

<dependency>
  <groupId>com.google.privacy.differentialprivacy</groupId>
  <artifactId>differentialprivacy</artifactId>
  <version>1.1.0</version>
</dependency>

Or use the Java artifact with other build systems.

Via the Go command

For the go building blocks library:

go get github.com/google/differential-privacy/[email protected]

For Privacy on Beam:

go get github.com/google/differential-privacy/[email protected]
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