Privacy Preserving Cyber Threat Intelligence Sharing Framework for Encrypted Analytics

This research focuses on the creation of an encrypted Cyber Threat Intelligence (CTI) sharing framework that supports encrypted data analytics with privacy preservation. It aims to support analytical computation in a centralized node without allowing that node to see any of the plain-text data.To enable privacy preservation of the data and its users, we structured the data into a graph structure that allows traversal over the encrypted data. We used Ciphertext-Policy Attribute-Based Encryption (CPABE), Deterministic Encryption (DE), and Order Revealing Encryption(ORE) to ensure end-to-end encrypted sharing of Cyber threat data. In this work we also cover CYBersecurity information EXchange with Privacy (CYBEX-P) and CYBEX-P with Encrypted Analytics, the precursor projects onwhich the framework is based. Our research aims to solve one of the biggest problems that CTI sharing has: securing the privacy of the data once it leaves the user’s premises. We focus on eliminating attack surfaces present in centralized systems, that is, the attack surface attackers had over the Backend and the surface the Backend has against the system. We also focused on maintaining as many capabilities of a CTI sharing platform, that is, CTI sharing and centralized analytics