Network monitoring and protection often involve large-scale networks and many organizations. Data on observing and inferring large-scale events, however, may be collected locally by monitors. Organizations who own monitors and thus data are often reluctant to share information due to security and privacy concerns. Communities form where voluntary organizations join with a good will and contribute data to share. Questions arise: Under a given privacy constraint, what types of shared information would be effective for network monitoring and protection? How to quantify such information from large community-based data repositories? This work describes a large-scale community network, and the corresponding data sets. Metrics from information theory, i.e., Renyi Information Entropy is then introduced to measure the effectiveness of shared information under a common privacy constraint. In particular, two types of shared information are studied, one for centralized and the other for decentralized sharing. Real data from DShield is used as an example to show how effective the shared information is for inference through a large-scale community network.