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De-identification Tools
Approximate Minima Perturbation (AMP)
De-identification Keywords: Differential Privacy, Machine Learning
This work presents a novel algorithm called Approximate Minima Perturbation (AMP) for differentially private convex optimization, and an extensive empirical evaluation on real datasets of both AMP and a number of previous approaches for solving this problem. The Github repository contains Python implementations of AMP, noisy stochastic gradient descent, noisy Frank-Wolfe, objective perturbation, and two variants of output perturbation, as well as a number of benchmarks for generating experimental results.
Notes: The AMP algorithm and associated experimental results are described in an IEEE Symposium on Security and Privacy 2019 paper available here.
Affiliation/Organization(s) Contributing: Carnegie Mellon University; Boston University; University of California, Berkeley; University of California, Santa Cruz; Peking University
GitHub POC: @jnear
ARX Data Anonymization Tool
De-identification Keywords: Differential Privacy, K-Anonymity, Anonymization, Machine Learning
ARX is a comprehensive open source software for anonymizing sensitive personal data. It supports a wide variety of (1) privacy and risk models, (2) methods for transforming data and (3) methods for analyzing the usefulness of output data.
Affiliation/Organization(s) Contributing: TUM - Technical University of Munich
GitHub POC: @prasser
Differential Privacy Synthetic Data Challenge Algorithms
De-identification Keywords: Differential Privacy, Synthetic Data Generation
Participants in Match #3 of NIST's 2018 Public Safety Communications Research Differential Privacy Synthetic Data Challenge developed these open source algorithms as part of an effort to advance differential privacy. Participants were challenged to create new methods, or improve existing methods of data de-identification, while preserving the dataset’s utility for analysis. All solutions were required to satisfy the differential privacy guarantee, a provable guarantee of individual privacy protection. Participants used a data set of emergency response events occurring in San Francisco and a sub-sample of the IPUMS USA data for the 1940 U.S. Census. Contributions are listed in alphabetical order.
DP_WGAN-UCLANESL
This repo contains an implementation for the award-winning solution to the 2018 Differential Privacy Synthetic Data Challenge by team UCLANESL. Our solution has been awarded the 5th place in Match #3 of the challenge and an earlier version has also won the 4th place in Match #1. The solution trains a wasserstein generative adversarial network (w-GAN) that is trained on the real private dataset. Differentially private training is applied by sanitizing (norm clipping and adding Gaussian noise) the gradients of the discriminator. Once the model is trained, it can be used to generate synthetic dataset by feeding random noise into the generator.
Team Members: Prof. Mani Srivastava (@msrivastava) - Team Captain (Match 1 and Match 3), Moustafa Alzantot (@malzantot) - (Match 1 and Match 3), Nat Snyder (@natsnyder1) - Match 1, Supriyo Charkaborty (@supriyogit) - Match 1
DP_WGAN-UCLANESL on GitHub More Information Share Feedback
DPFieldGroups
This is the fourth place entry in the third round of the NIST Differential Privacy Synthetic Data Challenge. The goal of this challenge is to produce differentially private synthetic data while retaining as much useful information as possible about the original data set. Colorado census data from 1940 with 98 field columns were provided for algorithm development with census data from other states used for testing. This solution groups together fields which have been found to be highly correlated. For each of these groups, a histogram is created for the purpose of counting the number of occurrences of every possible combination of values of all fields in the group. For privatization, Laplacian noise is added to every bin with scale proportional to the number of groups / total epsilon. Synthetic data is generated by selecting a random bin for each group with probability weighted by these noisy bin counts. The field values corresponding to each group's selected bin are written out as a single row of synthetic data.
Team Member & Affiliation: John Gardner (no affiliation)
DPFieldGroups on GitHub Share Feedback
DPSyn
We present DPSyn, an algorithm for synthesizing microdata while satisfying differential privacy, and its instantiation to the dataset used in the competition, namely Public Use Microdata Sample (PUMS) of the 1940 USA Census Data.
Team Members & Affiliations: Ninghui Li (Purdue University), Zhikun Zhang (Zhejiang University), Tianhao Wang (Purdue University)
rmckenna
The first place entry in the third round of the NIST Differential Privacy Synthetic Data Challenge. The high-level idea is to (1) use the Gaussian mechanism to obtain noisy answers to a carefully selected set of counting queries (1, 2, and 3 way marginals) and (2) find a synthetic data set that approximates the true data with respect to those queries. The latter step is accomplished with [3], and the previous step uses ideas inspired by [1] and [2]. More specifically, this is done by calculating the mutual information (on the public dataset) for each pair of attributes and selecting the marginal queries that have high mutual information.
[1] Zhang, Jun, et al. "Privbayes: Private data release via bayesian networks." ACM Transactions on Database Systems (TODS) 42.4 (2017): 25.
[2] Chen, Rui, et al. "Differentially private high-dimensional data publication via sampling-based inference." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015.
[3] McKenna, Ryan, Daniel Sheldon, and Gerome Miklau. "Graphical-model based estimation and inference for differential privacy." Proceddings of the 36th International Conference on Machine Learning. 2019.
Team Member & Affiliation: Ryan McKenna (UMass Amherst)
rmckenna Algorithm on GitHub Share Feedback
Differentially Private Stochastic Gradient Descent (DP-SGD)
De-identification Keywords: Differential Privacy, Machine Learning
Train machine learning models with differential privacy by clipping and noising gradients during stochastic gradient descent.
Notes: Paper with full details: https://arxiv.org/abs/1607.00133
Affiliation/Organization(s) Contributing: Google
GitHub POC: @ilyamironov
DP-SGD on GitHub Share Feedback
Diffprivlib
De-identification Keywords: Differential Privacy, Machine Learning, Data Analytics
Diffprivlib is a general-purpose Python library for experimenting with, and, building tools for, differential privacy. Diffprivlib includes a number of algorithms for machine learning and data analytics with differential privacy off-the-shelf in the familiar Scikit-learn and Numpy syntax.
Notes: Introductory whitepaper
Affiliation/Organization(s) Contributing: IBM Research
GitHub POC and Email: @naoise-h | naoise@ibm.com
Diffprivlib on GitHub Share Feedback
Duet
De-identification Keywords: Differential Privacy, Verification of Algorithms, Machine Learning
Duet is a programming language which automatically derives (and checks) differential privacy bounds for programs written in the language. Duet is designed to support modern machine learning algorithms, and advanced variants of differential privacy in order to add minimal noise to algorithm results in order to ensure privacy.
Affiliation/Organization(s) Contributing: University of Vermont, University of California at Berkeley, University of Utah
GitHub User Serving as POC: @jnear
Ektelo
De-identification Keywords: Differential Privacy
Ektelo is a programming framework and system that aids programmers in developing differentially private programs with high utility. Ektelo can be used to author programs for a variety of statistical tasks that involve answering counting queries over a table of arbitrary dimension.
Notes: Ektelo is described in detail in a SIGMOD 2018 paper, titled "EKTELO: A Framework for Defining Differentially-Private Computations." https://dl.acm.org/citation.cfm?id=3196921
Affiliation/Organization(s) Contributing: UMass Amherst, Duke University, Colgate University
GitHub POC: @michaelghay
Google Differential Privacy Library
De-identification Keywords: Differential Privacy
Google's differential privacy library provides a set of building block components that allow developers to build differentially private applications in C++, Java, and Go. Furthermore, Google's DP library offers 'Privacy on Beam', an end-to-end implementation of differential privacy that helps developers perform operations in a differentially private manner. This codelab gives further insight.
Notes:
Affiliation/Organization(s) Contributing: Google
Email POC: dp-open-source@google.com
Google Differential Privacy on GitHub Share Feedback
GUPT: Privacy preserving data analysis made easy
De-identification Keywords: Differential Privacy, Machine Learning, Database Queries
The tool provides differential privacy guarantees to statistical/machine learning algorithms by treating the underlying algorithm as a black-box, and only relying on input/output signatures. It implements a variant of the celebrated sample and aggregate framework by Nissim, Rashkhodnikova, and Smith, 2007. The empirical evaluation shows that the system scores well on various learning tasks (like clustering and regression).
Notes: GUPT is described in detail in a SIGMOD 2012 paper, titled "GUPT: Privacy Preserving Data Analysis Made Easy." A PDF is available here.
Affiliation/Organization(s) Contributing: University of California, Berkeley; University of California, Santa Cruz; Cornell University
GitHub POC: @prashmohan
PixelDP
De-identification Keywords: Differential Privacy, Verification of Algorithms, Machine Learning, Adversarial Examples
Adversarial examples that fool prediction models are a new class of attacks introduced by machine learning deployments. PixelDP is the first certified defense that both offers provable guarantees of robustness against these attacks and scales to large models and datasets, such as Google’s Inception on the ImageNet dataset. PixelDP's design relies on a novel use of differential privacy at prediction time.
Notes: This IEEE S&P 2019 research paper describes PixelDP.
Affiliation/Organization(s) Contributing: Columbia University
GitHub POC: @matlecu
PixelDP on GitHub Share Feedback
Privacy Protection Application (PPA)
De-identification Keywords: K-Anonymity, Anonymization, Information Leakage, Algorithmic Fairness, Database Queries, Location Data
The Privacy Protection Application de-identifies databases that contain sequential geolocation data, sometimes called moving object databases. A record of a personally-owned vehicle’s route of travel is an example, but the tool can process other types of geolocation sequences. The application has a graphical user interface and operates on Linux, OS X, and Windows. Location suppression is the de-identification strategy used, and decisions about which locations to suppress are based on information theory. This strategy does not modify the precision of retained location information. One of the objectives is to produce data usable for vehicle safety analysis and transportation application development.
Notes: This tool treats static databases and has two versions. The main GUI versions uses a very efficient map matching strategy that may identify false roads for certain types of road structures. The tagged version (https://github.com/usdot-its-jpo-data-portal/privacy-protection-application/releases/tag/hmm-mm) uses a Hidden Markov Model map matching algorithm that is more accurate, but less efficient. This version is a command line tool that runs in Docker. Additionally, a streaming de-identification tool was developed for a USDOT Safety Pilot Study. This tool uses geofencing to identify locations that can be retained. It can also be found on GitHub: https://github.com/usdot-jpo-ode/jpo-cvdp
POC: carterjm@ornl.gov
Private Aggregation of Teacher Ensembles (PATE)
De-identification Keywords: Differential Privacy, Machine Learning
The PATE framework achieves differentially private learning by carefully coordinating the activity of several different ML models.
Notes: Papers with full details: https://arxiv.org/abs/1802.08908
Affiliation/Organization(s) Contributing: Google
GitHub POC: @npapernot
PATE Framework on GitHub Share Feedback