# Private Sequential Learning

arXiv: https://arxiv.org/abs/1805.02136 · HTML: https://mlsanity.com/paper/1805.02136/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2018.

## Built on by (most cited first, 5 of 5)

- [Differential Privacy in Personalized Pricing with Nonparametric Demand Models](https://mlsanity.com/paper/2109.04615/) (2021)
- [Learner-Private Convex Optimization](https://mlsanity.com/paper/2102.11976/) (2021)
- [Controlling Federated Learning for Covertness](https://mlsanity.com/paper/2308.08825/) (2023)
- [Optimal Query Complexity of Secure Stochastic Convex Optimization](https://mlsanity.com/paper/2104.01926/) (2021)
- [Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization](https://mlsanity.com/paper/2405.07415/) (2024)

## Abstract (arXiv)

We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, v^*, by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes the learner's queries, though not the responses, and tries to infer from them the value of v^*. The objective of the learner is to obtain an accurate estimate of v^* using only a small number of queries, while simultaneously protecting her privacy by making v^* provably difficult to learn for the adversary. Our main results provide tight upper and lower bounds on the learner's query complexity as a function of desired levels of privacy and estimation accuracy. We also construct explicit query strategies whose complexity is optimal up to an additive constant.