Blind Targeting: Personalization under Third-Party Privacy Constraints
盲式定向：第三方隐私约束下的个性化AI 解读 ↓
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盲式定向：第三方隐私约束下的个性化
- 研究问题
- 当广告平台仅允许少量带差分隐私噪声的聚合查询时，广告主如何探索数据并制定有效的个性化定向策略？
- 方法与思路
- 作者改造贝叶斯优化，通过积分更新后验，使查询对象由单点变为数据区域，并设计面向定向任务的信息获取函数。
- 材料陈述的贡献
- 摘要报告，在部分情形中策略实现无隐私限制定向潜力的97%—101%，直觉基准为33%，且与因果森林的差异不显著。
- 阅读关注点
- 应核对查询预算、隐私噪声和收益基准的定义，增益效应的识别条件，以及报告比例与统计比较对应的具体实验情形。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要中的比例有情境限制，差异不显著也不能证明方法等效。
原文摘要
Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of aggregate queries to a dataset, which is further protected by adding differentially private noise. This paper studies whether and how advertisers can design effective targeting policies within these restrictive privacy preserving data environments. To achieve this, I develop a probabilistic machine learning method based on Bayesian optimization, which facilitates dynamic data exploration. Since Bayesian optimization was designed to sample points from a function to find its maximum, it is not applicable to aggregate queries and to targeting. Therefore, I introduce two innovations: (i) integral updating of posteriors which allows to select the best regions of the data to query rather than individual points and (ii) a targeting-aware acquisition function that dynamically selects the most informative regions for the targeting task. I identify the conditions of the dataset and privacy environment that necessitate the use of such a "smart" querying strategy. I apply the strategic querying method to the Criteo AI Labs dataset for uplift modeling (Diemert et al., 2018) that contains visit and conversion data from 14M users. I show that an intuitive benchmark strategy only achieves 33% of the non-privacy-preserving targeting potential in some cases, while my strategic querying method achieves 97-101% of that potential, and is statistically indistinguishable from Causal Forest (Athey et al., 2019): a state-of-the-art non-privacy-preserving machine learning targeting method.