Guidelines for Implementing and Auditing Differentially Private Systems

TL;DR

This paper offers best practices, testing techniques, and guidelines for developing and auditing differentially private systems to ensure privacy guarantees while maximizing data utility, applicable across various platforms.

Contribution

It provides a comprehensive set of guidelines, testing methods, and parameter recommendations for implementing and auditing differential privacy in diverse systems.

Findings

Established best practices for differential privacy implementation

Developed unit testing techniques specific to differential privacy

Provided guidelines for correct application and parameter settings

Abstract

Differential privacy is an information theoretic constraint on algorithms and code. It provides quantification of privacy leakage and formal privacy guarantees that are currently considered the gold standard in privacy protections. In this paper we provide an initial set of "best practices" for developing differentially private platforms, techniques for unit testing that are specific to differential privacy, guidelines for checking if differential privacy is being applied correctly in an application, and recommendations for parameter settings. The genesis of this paper was an initiative by Facebook and Social Science One to provide social science researchers with programmatic access to a URL-shares dataset. In order to maximize the utility of the data for research while protecting privacy, researchers should access the data through an interactive platform that supports differential privacy. The intention of this paper is to provide guidelines and recommendations that can generally be re-used in a wide variety of systems. For this reason, no specific platforms will be named, except for systems whose details and theory appear in academic papers.