  In the past decade, the information security and threat landscape has grown
significantly making it difficult for a single defender to defend against all
attacks at the same time. This called for introduc- ing information sharing, a
paradigm in which threat indicators are shared in a community of trust to
facilitate defenses. Standards for representation, exchange, and consumption of
indicators are pro- posed in the literature, although various issues are
undermined. In this paper, we rethink information sharing for actionable
intelli- gence, by highlighting various issues that deserve further explo-
ration. We argue that information sharing can benefit from well- defined use
models, threat models, well-understood risk by mea- surement and robust
scoring, well-understood and preserved pri- vacy and quality of indicators and
robust mechanism to avoid free riding behavior of selfish agent. We call for
using the differential nature of data and community structures for optimizing
sharing.
