(This project is deprecated and not maintained.)
This repository contains a query analysis and rewriting framework to enforce differential privacy for general-purpose SQL queries. The rewriting engine can automatically transform an input query into an intrinsically private query which embeds a differential privacy mechanism in the query directly; the transformed query enforces differential privacy on its results and can be executed on any standard SQL database. This approach supports many state-of-the-art differential privacy mechanisms; the code currently includes rewriters based on Elastic Sensitivity and Sample and Aggregate, and more will be added soon.
The rewriting framework is built on a robust dataflow analyses engine for SQL queries. This framework provides an abstract representation of queries, plus several kinds of built-in dataflow analyses tailored to this representation. This framework can be used to implement other types of dataflow analyses, as described below.
This framework is written in Scala and built using Maven. The code has been tested on Mac OS X and Linux. To build the code:
$ mvn package
The file examples/QueryRewritingExample.scala contains sample code for query rewriting and demonstrates the supported
mechanisms using a few simple queries. To run this example:
mvn exec:java -Dexec.mainClass="examples.QueryRewritingExample"
This example code can be easily modified, e.g., to test different queries or change parameter values.
Elastic sensitivity is an approach for efficiently approximating the local sensitivity of a query, which can be used to enforce differential privacy for the query. The approach requires only a static analysis of the query and therefore imposes minimal performance overhead. Importantly, it does not require any changes to the database. Details of the approach are available in this paper.
Elastic sensitivity can be used to determine the scale of random noise necessary to make the results of a query differentially private. For a given output column of a query with elastic sensitivity s, to achieve differential privacy for that column it suffices to smooth s according to the smooth sensitivity approach to obtain S, then add random noise drawn from the Laplace distribution, scaled to (S/epsilon) and centered at 0, to the true result of the query. The smoothing can be accomplished using the smooth sensitivity approach introduced by Nissim et al.
The file examples.ElasticSensitivityExample contains code demonstrating this approach directly (i.e., applying noise manually rather than generating an intrinsically private query).
To run this example:
mvn exec:java -Dexec.mainClass="examples.ElasticSensitivityExample"
This framework can perform additional analyses on SQL queries, and can be extended with new analyses.
Each analysis in this framework extends the base class com.uber.engsec.dp.sql.AbstractAnalysis.
To run an analysis on a query, call the method com.uber.engsec.dp.sql.AbstractAnalysis.analyzeQuery.
The parameter of this method is a string containing a SQL query, and its return value is an abstract domain representing
the results of the analysis.
The source code includes several example analyses to demonstrate features of the framework. The simplest example is com.uber.engsec.dp.analysis.taint.TaintAnalysis, which returns an abstract domain containing information about which output columns of the query might contain data flowing from "tainted" columns in the database. The database schema determines which columns are tainted. You can invoke this analysis as follows:
scala> (new com.uber.engsec.dp.analysis.taint.TaintAnalysis).analyzeQuery("SELECT my_col1 FROM my_table")
BooleanDomain = my_col1 -> FalseThis code includes several built-in analyses, including:
- The elastic sensitivity analysis, available in
com.uber.engsec.dp.analysis.differential_privacy.ElasticSensitivityAnalysis, returns an abstract domain (com.uber.engsec.dp.analysis.differential_privacy.SensitivityDomain) that maps each output column of the query to its elastic sensitivity. com.uber.engsec.dp.analysis.columns_used.ColumnsUsedAnalysislists the original database columns from which the results of each output column are computed.com.uber.engsec.dp.analysis.histogram.HistogramAnalysislists the aggregation-ness of each output column of the query (i.e. whether or not the output is an aggregation, and if so, which type).com.uber.engsec.dp.analysis.join.JoinKeysUsedlists the original database columns used as equijoin keys for each output column of the query.
New analyses can be implemented by extending one of the abstract analysis classes and implementing transfer functions
which describe how to update the analysis state for relevant query constructs. Analyses are written to update a
specific type of abstract domain which represents the current state of the analysis. Each abstract domain type
implements the trait com.uber.engsec.dp.dataflow.AbstractDomain.
The simplest way to implement a new analysis is to use com.uber.engsec.dp.dataflow.dp.column.AbstractColumnAnalysis,
which automatically tracks analysis state for each column of the query independently. Most of the example analyses are
of this type.
New analyses can be invoked in the same way as the built-in example analyses.
Please report security bugs through HackerOne.
This project is released under the MIT License.
This project is developed and maintained by Noah Johnson and Joe Near.