在用户隐私保护下优化广告投放效果，平衡隐私与广告效率
Reach Measurement, Optimization and Frequency Capping In Targeted Online Advertising Under k-Anonymity
- 用概率折扣法替代传统频次限制，适应隐私保护需求
- 引入隐私后广告效果明显下降，但平台成本增加有限
- 为广告主提供可量化的隐私友好型投放方案，适合关注用户隐私的平台
近年来，随着社交媒体的普及，在线广告在提升品牌认知度方面发挥重要作用。其中关键的技术是频次限制，即控制广告对特定用户的展示次数。然而，随着行业向注重用户隐私的广告解决方案转型，这一技术正面临挑战。本文研究在k-匿名隐私保护模型下的广告覆盖范围测量与优化问题。我们提出如何在新隐私环境下报告覆盖范围，并展示概率折扣——一种对传统频次限制的概率化改进——可用于优化广告活动表现。实验评估了用户隐私与广告有效性之间的权衡。结果表明，引入隐私保护后广告性能显著下降，但广告平台为此提供更高隐私保障的成本增加有限。
原文摘要 · Abstract (English)
The growth in the use of online advertising to foster brand awareness over recent years is largely attributable to the ubiquity of social media. One pivotal technology contributing to the success of online brand advertising is frequency capping, a mechanism that enables marketers to control the number of times an ad is shown to a specific user. However, the very foundation of this technology is being scrutinized as the industry gravitates towards advertising solutions that prioritize user privacy. This paper delves into the issue of reach measurement and optimization within the context of $k$-anonymity, a privacy-preserving model gaining traction across major online advertising platforms. We outline how to report reach within this new privacy landscape and demonstrate how probabilistic discounting, a probabilistic adaptation of traditional frequency capping, can be employed to optimize campaign performance. Experiments are performed to assess the trade-off between user privacy and the efficacy of online brand advertising. Notably, we discern a significant dip in performance as long as privacy is introduced, yet this comes with a limited additional cost for advertising platforms to offer their users more privacy.
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