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Differential Privacy over SQL

Table of Contents

About The Project

Differential Privacy over SQL (DPSQL) is a system for answering queries over differential privacy.

The file structure is as below

project
│   
└───config
└───docs
└───Profile
└───src
│   └───algorithm
└───Test
│   └───TPCH
│   └───Graph
└───Sample

./config stores the configuration files users need for the system.

./docs stores the reference information users need to work with DPSQL:

./Profile stores the Profile information for using mosek in the system.

./src stores main source files.

  • ./src/algorithm stores 3 algorithm we integrated into this system.

./Test stores the queries used in the experiments of the system.

./Sample stores the script for setting up database and collecting experiment results.

Prerequisites

Tools

Before running this project, please install below tools

Please do not install Cplex dependency, which can only handle a small dataset, but download the Cplex API and import that to python with this instruction.

Python Dependency

Here are dependencies used in python programs:

  • matplotlib
  • numpy
  • sys
  • os
  • collections
  • configparser
  • math
  • psycopg2
  • pglast
  • argparser

Database permission

The user should have the permission to read the schema of the database to use this system.

System structure

TODO

Demo System

To run the system, run main.py. There are seven parameters

  • --d: path to database initialization file;
  • --q: path to query file;
  • --r: path to private relation file;
  • --c: path to the configuration file;
  • --o: path to the output file;
  • --debug: debug mode for more information;
  • --optimal: choose to use optimal algorithm for SJA queries;

One can use --h to get help for parameter instruction.

For more information about input file, users can consult here

For the SQL syntax used in this system, users can consult here

Example:

python main.py --d ./config/database.ini --q ./test.txt --r ./test_relation.txt --c ./config/parameter.config --o out.txt

collect result

  1. install the dependency

  2. create an empty database in PosgreSQL

  3. generate tbl data files by using dbgen from TPCH website and store them in /Sample/data/TPCH

  4. run script we provide in /Sample/setupDBTPCH.py

python setupDBTPCH.py --db databasename
  1. run script we provide in /Sample/collectResult.py
python collectResult.py
  1. find the result in /Sample/result/TPCH

Future Plan

  • Distinct count queries type (projection);
  • User Interface
  • Better user experience;
  • Optimization;