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Evaluation of Google's differential privacy tool on a real world database

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Privacy Preservation Analytics

Evaluation of Google's differential privacy tool, along with the modifications made, on a real world database.

-> A basic, non-technical introduction to PPA and differential privacy: https://desfontain.es/privacy/differential-privacy-awesomeness.html

-> The tool can be found at: https://github.com/google/differential-privacy (Only the modified wrapper is present in the repo, so the tool has to be built before cloning)

-> The differentially privacy paper can be found at: https://arxiv.org/pdf/1909.01917.pdf

-> Tool Requirements:

- Linux OS

- Bazel v.3

- PostgreSQL v.11.0

- Python 3.6 or higher

Note: Fast and stable internet is absolutely necessary in order to build the tool successfully. 

-> Modifications made include:

- Conversion of normal SQL queries to the intrinsically private queries that the tool requires

- Improvement in performance overhead using a different mechanism to calculate lower and upper bounds.

-> Database.zip contains the SQL files to create the testing database.

Working:

  • To run a normal query: python3 Privacy.py <database name> "<Query without the ';' at the end>"
  • To run the test script (only works for the UBER database: python3 test_privacy.py "<test input file that has all the queries>"
  • To run the modified WWM file: python3 privacy_wwm.py <database name> "<Query without the ';' at the end>"
  • To run the string matching modified file: python3 str_privacy.py <database name> "<Query without the ';' at the end>"

Authors

  • Adithi Satish
  • Avnish Goel
  • Manah Shetty

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Evaluation of Google's differential privacy tool on a real world database

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