e cyber-security threat landscape is a faceless whirlwind of deliberate and persistent attempts to compromise individual and organizational data toward nefarious ends. We might not know the direction of the next threat, but we certainly know it is inevitable. All organizations are faced with the massive problem of evolving threat; however, due to the current drought of cyber-security and data science professionals this problem can be prohibitively expensive to solve in isolation. In the absence of shared intelligence, best efforts can lead to ineffective ad-hoc security systems which are only token in nature. The dichotomy is as such: hackers and cyber-criminals can share intelligence for their purposes; how can we leverage and share intelligence about these common threats without compromising the data integrity of each organization? How do we detect advanced persistent threats without violating the privacy rights of individual users? In this talk, we discuss the state of the science in privacy-preserving threat analytics scaled to massive data sets and propose a solution to this dichotomy.