Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Collaborative security initiatives are increasingly often ad-<lb>vocated to improve timeliness and effectiveness of threat mit-<lb>igation. Among these, collaborative predictive blacklisting<lb>(CPB) aims to forecast attack sources based on alerts con-<lb>tributed by multiple organizations that might be targeted in<lb>similar ways. Alas, CPB proposals thus far have only fo-<lb>cused on improving hit counts, but overlooked the impact of<lb>collaboration on false positives and false negatives. More-<lb>over, sharing threat intelligence often prompts important pri-<lb>vacy, confidentiality, and liability issues. In this paper, we<lb>first provide a comprehensive measurement analysis of two<lb>state-of-the-art CPB systems: one that uses a trusted cen-<lb>tral party to collect alerts [Soldo et al., Infocom’10] and a<lb>peer-to-peer one relying on controlled data sharing [Freudi-<lb>ger et al., DIMVA’15], studying the impact of collaboration<lb>on both correct and incorrect predictions. Then, we present<lb>a novel privacy-friendly approach that significantly improves<lb>over previous work, achieving a better balance of true and<lb>false positive rates, while minimizing information disclosure.<lb>Finally, we present an extension that allows our system to<lb>scale to very large numbers of organizations.

Building and Measuring Privacy-Preserving Predictive Blacklists