VAP: Online Data Valuation and Pricing for Machine Learning Models in Mobile Health

Mobile health (mHealth) applications, benefiting from mobile computing, have generated numerous mHealth data. However, they are dispersed across isolated devices, which hinders discovering insights underlying the aggregated data. Considering the online characteristics of mHealth, in this work, we present the first online data
VA
luation and
P
ricing mechanism, namely VAP, to incentive users to contribute mHealth data for machine learning (ML) tasks in mHealth systems. Under the Bayesian framework, we propose a new metric based on the concept of entropy to calculate 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. Furthermore, to tackle the budget constraint, we incorporate a two-stage multi-armed bandit with a knapsack method. We also extend VAP to advanced ML models by computing the entropy on the prediction space. Finally, we have evaluated VAP on two real-world mHealth data sets. Evaluation results show that VAP outperforms the state-of-the-art data valuation and pricing mechanisms in terms of computational complexity and extracted profit.