Privacy Auctions for Recommender Systems

TL;DR

This paper introduces a privacy auction framework for recommender systems, balancing privacy costs and prediction accuracy, and proposes mechanisms that are near-optimal in truthfulness and efficiency.

Contribution

It formalizes the privacy-accuracy trade-off in private data markets for recommender systems and designs a near-optimal truthful auction mechanism under budget constraints.

Findings

A simple class of estimates achieves an order-optimal privacy-accuracy trade-off.

The proposed mechanism is 5-approximate in accuracy compared to the optimal.

No truthful mechanism can achieve better than a 2-ε approximation.

Abstract

We study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a \emph{privacy auction}. Motivated by recommender systems, the statistic we consider is a linear predictor function with publicly known weights. The statistic can be viewed as a prediction of the unknown data of a new individual, based on the data of individuals in the database. We formalize the trade-off between privacy and accuracy in this setting, and show that a simple class of estimates achieves an order-optimal trade-off. It thus suffices to focus on auction mechanisms that output such estimates. We use this observation to design a truthful, individually rational, proportional-purchase mechanism under a fixed budget constraint. We show that our mechanism is 5-approximate in terms of accuracy compared to the optimal mechanism, and that no truthful mechanism can achieve a $2-\varepsilon$ approximation, for any $\varepsilon > 0$.