  The widespread availability of behavioral data has led to the development of
data-driven personalized pricing algorithms: sellers attempt to maximize their
revenue by estimating the consumer's willingness-to-pay and pricing
accordingly. Our objective is to develop algorithms that protect consumer
interests against personalized pricing schemes. In this paper, we consider a
consumer who learns more and more about a potential purchase across time, while
simultaneously revealing more and more information about herself to a potential
seller. We formalize a strategic consumer's purchasing decision when
interacting with a seller who uses personalized pricing algorithms, and
contextualize this problem among the existing literature in optimal stopping
time theory and computational finance. We provide an algorithm that consumers
can use to protect their own interests against personalized pricing algorithms.
This algorithmic stopping method uses sample paths to train estimates of the
optimal stopping time. To the best of our knowledge, this is one of the first
works that provides computational methods for the consumer to maximize her
utility when decision making under surveillance. We demonstrate the efficacy of
the algorithmic stopping method using a numerical simulation, where the seller
uses a Kalman filter to approximate the consumer's valuation and sets prices
based on myopic expected revenue maximization. Compared to a myopic purchasing
strategy, we demonstrate increased payoffs for the consumer in expectation.
