  The society's insatiable appetites for personal data are driving the
emergency of data markets, allowing data consumers to launch customized queries
over the datasets collected by a data broker from data owners. In this paper,
we study how the data broker can maximize her cumulative revenue by posting
reasonable prices for sequential queries. We thus propose a contextual dynamic
pricing mechanism with the reserve price constraint, which features the
properties of ellipsoid for efficient online optimization, and can support
linear and non-linear market value models with uncertainty. In particular,
under low uncertainty, our pricing mechanism provides a worst-case regret
logarithmic in the number of queries. We further extend to other similar
application scenarios, including hospitality service, online advertising, and
loan application, and extensively evaluate three pricing instances of noisy
linear query, accommodation rental, and impression over MovieLens 20M dataset,
Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset,
respectively. The analysis and evaluation results reveal that our proposed
pricing mechanism incurs low practical regret, online latency, and memory
overhead, and also demonstrate that the existence of reserve price can mitigate
the cold-start problem in a posted price mechanism, and thus can reduce the
cumulative regret.
