Online Data Valuation and Pricing for Machine Learning Tasks in Mobile Health

Mobile health (mHealth) applications, benefiting from mobile computing, have emerged rapidly in recent years, and generated a large volume of mHealth data. However, these valuable data are dispersed across isolated devices or organizations, which hinders discovering insights underlying the aggregated data. Considering the online characteristics of mHealth tasks, there is an urgent need for online data acquisition. In this paper, we present the first online data Valuation And Pricing mechanism, namely VAP, to incentive users to contribute mHealth data for machine learning (ML) tasks in mHealth systems. Under the framework of Bayesian ML, we propose a new metric based on the concept of entropy, to evaluate data valuation during model training in an online manner. In proportion to the data valuation, we then determine payments as compensations for users to contribute their data. We formulate this pricing problem as a contextual multi-armed bandit with the goal of profit maximization and propose a new algorithm based on the characteristics of pricing. We also extend VAP to general ML models. Finally, we have evaluated VAP on two real-world mHealth data sets. Evaluation results show that VAP outperforms the state-of-the-art valuation and pricing mechanisms in terms of computational complexity and extracted profit.