Privacy-preserving dynamic personalized pricing with demand learning
The prevalence of e-commerce has made customers' detailed personal information readily
accessible to retailers, and this information has been widely used in pricing decisions. When …
accessible to retailers, and this information has been widely used in pricing decisions. When …
No peek: A survey of private distributed deep learning
We survey distributed deep learning models for training or inference without accessing raw
data from clients. These methods aim to protect confidential patterns in data while still …
data from clients. These methods aim to protect confidential patterns in data while still …
Differential privacy in personalized pricing with nonparametric demand models
In recent decades, the advance of information technology and abundant personal data
facilitate the application of algorithmic personalized pricing. However, this leads to the …
facilitate the application of algorithmic personalized pricing. However, this leads to the …
Privacy-preserving personalized revenue management
This paper examines how data-driven personalized decisions can be made while
preserving consumer privacy. Our setting is one in which the firm chooses a personalized …
preserving consumer privacy. Our setting is one in which the firm chooses a personalized …
Controlling federated learning for covertness
A Jain, V Krishnamurthy - arXiv preprint arXiv:2308.08825, 2023 - arxiv.org
A learner aims to minimize a function $ f $ by repeatedly querying a distributed oracle that
provides noisy gradient evaluations. At the same time, the learner seeks to hide $\arg\min f …
provides noisy gradient evaluations. At the same time, the learner seeks to hide $\arg\min f …
Privacy-preserving personalized recommender systems
Abstract Problem Definition: Personalized product recommendations are essential for online
platforms, but they raise privacy concerns due to the risk of inference attacks. To address this …
platforms, but they raise privacy concerns due to the risk of inference attacks. To address this …
Optimal query complexity for private sequential learning against eavesdropping
We study the query complexity of a learner-private sequential learning problem, motivated
by the privacy and security concerns due to eavesdropping that arise in practical …
by the privacy and security concerns due to eavesdropping that arise in practical …
Query complexity of Bayesian private learning
K Xu - Advances in Neural Information Processing Systems, 2018 - proceedings.neurips.cc
We study the query complexity of Bayesian Private Learning: a learner wishes to locate a
random target within an interval by submitting queries, in the presence of an adversary who …
random target within an interval by submitting queries, in the presence of an adversary who …
Optimal query complexity of secure stochastic convex optimization
We study the\emph {secure} stochastic convex optimization problem: a learner aims to learn
the optimal point of a convex function through sequentially querying a (stochastic) gradient …
the optimal point of a convex function through sequentially querying a (stochastic) gradient …
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization
A Jain, V Krishnamurthy - IEEE Control Systems Letters, 2024 - ieeexplore.ieee.org
This paper studies how a stochastic gradient algorithm (SG) can be controlled to hide the
estimate of the local stationary point from an eavesdropper. Such problems are of significant …
estimate of the local stationary point from an eavesdropper. Such problems are of significant …