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Abstract:
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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 performance of branding and to improve the budget allocation strategy. However, cross-publisher measurement is non-trivial particularly under strict differential-privacy restrictions. This paper introduces the first locally-differentially-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 with the standard definition of differential privacy, the solution prevents attackers from telling if any specific user is reached or not with a given level of confidence. The solution enjoys particularly high accuracy for the estimation between two publishers. For more than two publishers, the solution enjoys small variance, at a risk of having bias in the presence of cross-publisher correlation of user activity.
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