# Blind Targeting: Personalization under Third-Party Privacy Constraints

arXiv: https://arxiv.org/abs/2507.05175 · HTML: https://mlsanity.com/paper/2507.05175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023)
- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Differential Privacy: An Economic Method for Choosing Epsilon](https://mlsanity.com/paper/1402.3329/) (2014)
- [Linear Program Reconstruction in Practice](https://mlsanity.com/paper/1810.05692/) (2018)
- [Multitask Learning and Bandits via Robust Statistics](https://mlsanity.com/paper/2112.14233/) (2021)
- [PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn](https://mlsanity.com/paper/1809.07754/) (2018)
- [Spending Privacy Budget Fairly and Wisely](https://mlsanity.com/paper/2204.12903/) (2022)
- [Fixed-Budget Differentially Private Best Arm Identification](https://mlsanity.com/paper/2401.09073/) (2024)
- [Scalable Analysis of Bipartite Experiments](https://mlsanity.com/paper/2402.11070/) (2024)

## Abstract (arXiv)

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.