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Turbo

Turbo is an effective cache for linear query workloads over DP databases. Turbo builds upon private multiplicative weights (PMW), a DP mechanism that is powerful in theory but very ineffective in practice, and transforms it into a highly effective caching object, namely PMW-Bypass. A description of this project can be found on our paper, titled Turbo: Effective caching in Differentially-Private Databases and published at SOSP '23.

Repo Structure

  • datasets: Contains a small version of CitiBike, a dataset we used consistently for the evaluation of Turbo (see turbo-suite/turbo-sosp-artifact).

  • turbo-lib: Contains the main functionality of Turbo e.g. implementaion of caches, formulas for converting privacy budget to accuracy, a default configuration for Turbo and the main Turbo class which users need to instantiate in order to integrate with Turbo.

  • turbo-tumult: An integration of Turbo with Tumult. See turbo-tumult/example.ipynb for an example on how to use Tumult with Turbo.

  • turbo-sql: A simplistic library with some basic functionality that facilitates the integration of Turbo with a SQL dp-engine.

  • dummy-dp-engine: An implementation of a dummy SQL DP engine and Turbo's integration with it. See dummy-dp-engine/example.ipynb for an example on how it works.

  • turbo-sosp-artifact: The official artifact for SOSP23. This version of Turbo contains a lot of additional functionality apart from the main caching components. It comprises a workload generator for running experiments on simulations, the datasets and benchmarks (automated scripts) that we used for evaluation and a lightweight, internal DP executor for running queries. It provides instructions on how to reproduce all the experiments found in the evaluation section of the paper.

Setup

Start a new Python virtual environment (e.g. with poetry shell).

Install local packages and dependencies:

pip3 install -r requirements.txt

Check installation:

python3 turbo-tumult/tmlt/turbo/example.py