Privacy-centric Cross-publisher Reach and Frequency Estimation Via Vector of Counts
Abstract
Reach and frequency are two of the most important metrics in advertising management. Ads are distributed to different publishers with a hope to maximize the reach at effective frequency. Reliable cross-publisher reach and frequency measurement is called for, to assess the actual ROI of branding and to improve the budget allocation strategy. However, cross-publisher measurement is non-trial under the strict privacy restriction.
This paper introduces the first locally differential private solution in the literature to cross-publisher reach and frequency estimation. The solution consists of a family of algorithms based on a data structure called Vector of Counts (VoC). Complying the standard of differential privacy, the solution prevents attackers from telling if any user is reached or not with enough confidence. The solution enjoys particularly high accuracy for the estimation of two publishers. For more than two publishers, the solution does a careful bias-variance trade-off. It enjoys small variance, at a risk of having bias in the presence of cross-publisher correlation of user activity.
This paper introduces the first locally differential private solution in the literature to cross-publisher reach and frequency estimation. The solution consists of a family of algorithms based on a data structure called Vector of Counts (VoC). Complying the standard of differential privacy, the solution prevents attackers from telling if any user is reached or not with enough confidence. The solution enjoys particularly high accuracy for the estimation of two publishers. For more than two publishers, the solution does a careful bias-variance trade-off. It enjoys small variance, at a risk of having bias in the presence of cross-publisher correlation of user activity.
Research Areas
