Spotlight
Local Differential Privacy for Evolving Data
Matthew Joseph · Aaron Roth · Jonathan Ullman · Bo Waggoner

Tue Dec 4th 03:35 -- 03:40 PM @ Room 517 CD

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.

Author Information

Matthew Joseph (University of Pennsylvania)
Aaron Roth (University of Pennsylvania)
Jonathan Ullman (Northeastern University)
Bo Waggoner (Microsoft)

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