Privacy-Preserving Quantile Treatment Effect Estimation for Randomized Controlled Trials
AuthorsLeon Yao, Paul Yiming Li, Jiannan Lu
AuthorsLeon Yao, Paul Yiming Li, Jiannan Lu
In accordance with the principle of "data minimization," many internet companies are opting to record less data. However, this is often at odds with A/B testing efficacy. For experiments with units with multiple observations, one popular data-minimizing technique is to aggregate data for each unit. However, exact quantile estimation requires the full observation-level data. In this paper, we develop a method for approximate Quantile Treatment Effect (QTE) analysis using histogram aggregation. In addition, we can also achieve formal privacy guarantees using differential privacy.
October 25, 2021research area Data Science and Annotation, research area Privacyconference CODE
Amidst rising appreciation for privacy and data usage rights, researchers have increasingly acknowledged the principle of data minimization, which holds that the accessibility, collection, and retention of subjects' data should be kept to the bare amount needed to answer focused research questions. Applying this principle to randomized controlled trials (RCTs), this paper presents algorithms for making accurate inferences from RCTs under...
September 21, 2021research area Privacyconference IEEE BITS the Information Theory Magazine
The privacy risk has become an emerging challenge in both information theory and computer science due to the massive (centralized) collection of user data. In this paper, we overview privacy-preserving mechanisms and metrics from the lenses of information theory, and unify different privacy metrics, including f-divergences, Renyi divergences, and differential privacy, by the probability likelihood ratio (and the logarithm of it). We introduce...