Privacy-Resolution Tradeoff for Adaptive Noisy Twenty Questions Estimation
Chunsong Sun, Lin Zhou

TL;DR
This paper investigates the balance between privacy and accuracy in adaptive noisy twenty questions estimation, proposing a new private query method that improves performance over previous approaches, especially in noiseless scenarios.
Contribution
It introduces a two-stage private query procedure for noisy twenty questions estimation, analyzing its performance and demonstrating improvements over prior methods in noiseless cases.
Findings
Proposed a two-stage private query procedure for noisy estimation.
Analyzed non-asymptotic and asymptotic performance of the method.
Achieved better performance than previous methods in noiseless scenarios.
Abstract
We revisit noisy twenty questions estimation and study the privacy-resolution tradeoff for adaptive query procedures. Specifically, in twenty questions estimation, there are two players: an oracle and a questioner. The questioner aims to estimate target variables by posing queries to the oracle that knows the variables and using noisy responses to form reliable estimates. Typically, there are adaptive and non-adaptive query procedures. In adaptive querying, one designs the current query using previous queries and their noisy responses while in non-adaptive querying, all queries are posed simultaneously. Generally speaking, adaptive query procedures yield better performance. However, adaptive querying leads to privacy concerns, which were first studied by Tsitsiklis, Xu and Xu (COLT 2018) and by Xu, Xu and Yang (AISTATS 2021) for the noiseless case, where the oracle always provides…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Machine Learning and Algorithms
