DProvDB

Main repository for "DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance", accepted to appear in Proc. of the ACM on Management of Data (PACMMOD/SIGMOD'2024) [bibtex] [tech report]

Brief Intro

DProvDB projects aims to build an online DP query processing system where multiple data analysts (with different trust levels) are involved -- these data analysts are not allowed to collude by law or regulations but have the incentive to collude (for a more accurate query answer). We would like to develop DP algorithms that can minimize the privacy loss when the analysts are compromised and build a system – DProvDB – to maximize the total number of queries that can be answered given a fixed privacy budget.

This repository contains the implementation of the DProvDB system.

Repository Structure

├── DProvDB/
│   ├── src/                              *Main directory
|       ├── main/scala/DProvDB/           *System source code
|       └── test/resources/schema.yaml    *Database configuration
│   ├── data/                             *Directory to dataset and place experimental results
|   ├── chorus/                           *Chorus submodule
|   ├── DProvDB_full.pdf                  *Technical report
|   └── build.sbt                         *Project dependency

Code Guide

Prerequisite:

Database: Postgresql

Java packaging (MVN and SBT) latest version

scalaVersion := "2.12.2"

Step 0: Clone this repo and open

(skipped :)

Step 1: installing submodule (Chorus)

Clone Chorus from their github repo:

git submodule add https://github.com/uvm-plaid/chorus.git chorus

git submodule update --init --recursive

Install Chorus:

cd chorus

mvn install

Note: potential missing dependency for Chorus. Adding the following to the SBT file.

libraryDependencies += "com.google.guava" % "guava" % "28.0-jre"

Step 2: preparing data

1. Configuring Postgres DB

createdb adult

psql adult

Create dataset schema and load data.

CREATE TABLE adult (AGE INTEGER NOT NULL,
WORKCLASS VARCHAR(55) NOT NULL,
FNLWQT INTEGER NOT NULL,
EDUCATION VARCHAR(55) NOT NULL,
EDUCATION_NUM INTEGER NOT NULL,
MARITAL_STATUS VARCHAR(55) NOT NULL,
OCCUPATION VARCHAR(55) NOT NULL,
RELATIONSHIP VARCHAR(55) NOT NULL,
RACE VARCHAR(55) NOT NULL,
SEX VARCHAR(55) NOT NULL,
CAPITAL_GAIN INTEGER NOT NULL,
CAPITAL_LOSS INTEGER NOT NULL,
HOURS_PER_WEEK INTEGER NOT NULL,
NATIVE_COUNTRY VARCHAR(55) NOT NULL,
SALARY VARCHAR(55) NOT NULL);

\copy adult FROM './data/adult.data' DELIMITER ',' CSV

Then create a user, Link, with Postgres, and grant access:

CREATE USER link WITH PASSWORD '12345';
GRANT ALL PRIVILEGES ON TABLE adult TO link;

Similarly, one can import TPC-H dataset with TPC-H kit.

One can also use their own data with DProvDB, but the DB schema needs to be properly configured in 'src/test/resources/schema.yaml'.

Step 3: running experiments

We use SBT commandline arguments tool for specifying experiments.

sbt "run [args1] [args2] [args3] [args4]"

We enable 4 arguments:

• [args1]: dataset, must be "adult" or "tpch";
• [args2]: task, must be "RRQ" or "EQW";
• [args3]: table, e.g., "adult", or "orders";
• [args4]: 5 letters to decide which experiment(s) to run, "T" for run, "F" for not run. e.g., "TFTFT" meaning running all experiments except the 2nd and the 4th.

How to cite:

@inproceedings{zhang2024dprovdb,
  author={Zhang, Shufan and He, Xi},
  title={DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance}, 
  journal={Proceedings of the ACM on Management of Data (SIGMOD'2024)},
  url={https://arxiv.org/abs/2309.10240},
  note={to appear}
}

Correspondence

📬 Shufan Zhang 📜 Homepage
📬 Xi He 📜 Homepage

License

BSD-3-Clause License