Expanding Differentially Private Solutions: A Python Case Study

Thursday, June 23, 2022 - 10:00 am10:15 am

Vadym Doroshenko, Google


Differential privacy in practice proved to be hard: there are a lot of subtle, easy-to-get-wrong implementation nuances, it can be difficult to preserve data utility, choose optimal parameters, interpret results, etc.

In this talk we introduce PipelineDP, a tool that allows Python developers to produce differentially-private versions of their data processing pipelines. PipelineDP was designed with small and massive applications in mind: it can be run both locally and on scalable data processing such as Apache Spark or Beam. Our aspiration is that PipelineDP allows differential privacy to be deployed in production by engineers who are not experts in DP.

Vadym Doroshenko‎, Google

Vadym Doroshenko is a software engineer at Google where he works on building anonymizaiton infrastructure and helping teams to apply anonymization. He is tech lead at PipelineDP (pipelinedp.io). He is passionate about Differential Privacy research and in bringing it to production. He received his PhD in mathematics from Taras Shevchenko National University of Kyiv.

@conference {280294,
author = {Vadym Doroshenko‎},
title = {Expanding Differentially Private Solutions: A Python Case Study},
year = {2022},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun

Presentation Video