A Privacy Preserving Scalable Architecture for Collaborative Event Correlation

We propose an efficient software architecture for private collaborative event processing, enabling information sharing and processing among administratively and geographically disjoint organizations over the Internet. The architecture is capable of aggregating and correlating events coming from the organizations in near real-time, while preserving the privacy of sensitive data items even in the case of coalition of attackers. Although there is a rich literature in the field of secure multiparty computation techniques that preserve the privacy in a distributed systems, the ability of such systems to scale up horizontally (number of participants) and vertically (dataset per participant) is still limited. The key novelty of the architecture is the usage of a pseudo-random oracle functionality distributed among the organizations participating to the system for obfuscating the data, that allows for achieving a good level of privacy while guaranteing scalability in both dimensions. Some preliminary performance results are provided.