CrowdBuy: Privacy-friendly Image Dataset Purchasing via Crowdsourcing

Abstract

In recent years, advanced machine learning techniques have demonstrated remarkable achievements in many areas. Despite the great success, one of the bottlenecks in applying machine learning techniques in real world applications lies in the lack of a large amount of high-quality training data from diverse domains. Meanwhile, massive personal data is being generated by mobile devices and is often underutilized. To bridge the gap, we propose a general dataset purchasing framework, named CrowdBuy and CrowdBuy++, based on crowdsourcing, with which a buyer can efficiently buy desired data from available mobile users with quality guarantee in a way respecting users' data ownership and privacy. We present a complete set of tools including privacy-preserving image dataset quality measurements and image selection mechanisms, which are budget feasible, truthful and highly efficient for mobile users. We conducted extensive evaluations of our framework on large-scale images and demonstrate that the system is capable of crowdsourcing high quality datasets while preserving image privacy with little computation and communication overhead.