| commit | 6e74aef9e4c5ec3d55812554c194abe432275bcc | [log] [tgz] |
|---|---|---|
| author | Miraç Vuslat Başaran <miracvbasaran@gmail.com> | Mon Feb 07 15:39:28 2022 +0100 |
| committer | GitHub <noreply@github.com> | Mon Feb 07 15:39:28 2022 +0100 |
| tree | 76795a0c02bd8f602829033ea3467eaf8616637c | |
| parent | ec0b439933cd00a11e98ed1ef4bef1fde96c39f4 [diff] |
Fix a privacy-impacting bug in ThresholdedResult() function of dpagg.Count (#101) Fix a privacy-impacting bug in ThresholdedResult() function of dpagg.Count that leads to slightly higher than intended delta. The bug is a result of converting floating-point threshold to an integer. Consider the following to see how the bug would occur: Assume the noisy count is 37 and the threshold computed from noise parameters & threshold delta is 37.3. This means that we should not be returning the result. However, converting the threshold (37.3) to an int64 truncates the decimal part per go specification, making it 37. Threshold (37) is smaller than or equal to noisy result (37), so we return the result. To fix this, we round the threshold up to the nearest integer before converting it to an int64. Although this problem did not exist for dpagg.BoundedSumInt64, we modify the code there for consistency.
This repository contains libraries to generate ε- and (ε, δ)-differentially private statistics over datasets. It contains the following tools.
To get started on generating differentially private data, we recomend you follow the Privacy on Beam codelab.
Currently, the DP building block libraries support the following algorithms:
| Algorithm | C++ | Go | Java |
|---|---|---|---|
| Laplace mechanism | Supported | Supported | Supported |
| Gaussian mechanism | Supported | Supported | Supported |
| Count | Supported | Supported | Supported |
| Sum | Supported | Supported | Supported |
| Mean | Supported | Supported | Supported |
| Variance | Supported | Supported | Planned |
| Standard deviation | Supported | Supported | Planned |
| Quantiles | Supported | Supported | Supported |
| Automatic bounds approximation | Supported | Planned | Planned |
| Truncated geometric thresholding | Supported | Supported | Supported |
| Laplace thresholding | Supported | Supported | Supported |
| Gaussian thresholding | Planned | Supported | Supported |
Implementations of the Laplace mechanism and the Gaussian mechanism use secure noise generation. These mechanisms can be used to perform computations that aren't covered by the algorithms implemented in our libraries.
The DP building block libraries are suitable for research, experimental or production use cases, while the other tools are currently experimental, and subject to change.
In order to run the differential privacy library, you need to install Bazel in version 4.1.0, if you don't have it already. Follow the instructions for your platform on the Bazel website
You also need to install Git, if you don't have it already. Follow the instructions for your platform on the Git website.
Once you've installed Bazel and Git, open a Terminal and clone the differential privacy directory into a local folder:
git clone https://github.com/google/differential-privacy.git
Navigate into the differential-privacy folder you just created, and build the differential privacy library and dependencies using Bazel (note: ... is a part of the command and not a placeholder):
To build the C++ library, run:
cd cc bazel build ...
To build the Go library, run:
cd go bazel build ...
To build the Java library, run:
cd java bazel build ...
You may need to install additional dependencies when building the PostgreSQL extension, for example on Ubuntu you will need these packages:
sudo apt-get install make libreadline-dev bison flex
Differential privacy requires some bound on maximum number of contributions each user can make to a single aggregation. The DP building block libraries don‘t perform such bounding: their implementation assumes that each user contributes only a fixed number of rows to each partition. That number can be configured by the user. The library neither verifies nor enforces this limit; it is the caller’s responsibility to pre-process data to enforce this.
We chose not to implement this step at the DP building block level because it requires some global operation over the data: group by user, and aggregate or subsample the contributions of each user before passing them on to the DP building block aggregators. Given scalability constraints, this pre-processing must be done by a higher-level part of the infrastructure, typically a distributed processing framework: for example, Privacy on Beam relies on Apache Beam for this operation.
For more detail about our approach to building scalable end-to-end differential privacy frameworks, we recommend reading our paper about differentially private SQL, which describes such a system. Even though the interface of Privacy on Beam is different, it conceptually uses the same framework as the one described in this paper.
We will continue to publish updates and improvements to the library. We are happy to accept contributions to this project. Please follow our guidelines when sending pull requests. We will respond to issues filed in this project. If we intend to stop publishing improvements and responding to issues we will publish notice here at least 3 months in advance.
This is not an officially supported Google product.
We are always keen on learning about how you use this library and what use cases it helps you to solve. We have two communication channels:
A public discussion group where we will also share our preliminary roadmap, updates, events, etc.
A private email alias at dp-open-source@google.com where you can reach us directly about your use cases and what more we can do to help.
Please refrain from sending any personal identifiable information. If you wish to delete a message you've previously sent, please contact us.