As the hidden backbone of todays Internet, the Domain Name System (DNS) provides name resolution service for almost every networked application. To exploit the rich DNS query information for traffic engineering or user behavior analysis, both passive capturing and active probing techniques have been proposed in recent years. Despite its full visibility of DNS behaviors, the passive capturing technique suffers from prohibitive management cost and results in tremendous privacy concerns towards its large-scale and collaborative deployment. Comparatively, the active probing technique overcomes these limitations, providing broad-view and privacy-preserving DNS query analysis at the cost of constrained visibility of fine-grained DNS behavior. This paper aims to accurately estimate DNS query characteristics based on DNS cache activities, which can be acquired via active probing on a large scale at negligible management cost and minimized privacy concerns. Specifically, we have made three contributions: (1) we propose a novel solution, which integrates the renewal theory-based DNS caching formulation and the hyper-exponential distribution model. The solution offers great flexibility to model various domains; (2) we perform a large-scale real-world DNS trace measurement, and demonstrate that our solution significantly improves the estimation accuracy; (3) we apply our solution to estimate the malware-infected host population in remote management networks. The experimental results have demonstrated that our solution can achieve high estimation accuracy and outperforms the existing method.