Local Differential Privacy for Evolving Data

TL;DR

This paper introduces a novel local differential privacy technique that maintains accurate, up-to-date statistics over time despite evolving data distributions, with privacy guarantees depending on distribution changes rather than collection frequency.

Contribution

It presents a new method for local differential privacy that handles evolving data, enabling continuous statistical tracking with improved long-term privacy guarantees.

Findings

Effective tracking of changing statistics over time.

Privacy guarantees degrade with distribution changes, not collection periods.

Applicable to frequency and heavy-hitter estimation.

Abstract

There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use. As a result, these systems do not provide meaningful privacy guarantees over long time scales. Moreover, existing techniques to mitigate this effect do not apply in the "local model" of differential privacy that these systems use. In this paper, we introduce a new technique for local differential privacy that makes it possible to maintain up-to-date statistics over time, with privacy guarantees that degrade only in the number of changes in the underlying distribution rather than the number of collection periods. We use our technique for tracking a changing statistic in the setting where users are partitioned into an unknown collection of groups, and at every time period each user draws a single bit from a common (but changing) group-specific distribution. We also provide an application to frequency and heavy-hitter estimation.