Techniques for Content Subscription Anonymity with Distributed Brokers

When issuing a one-shot or continuous content-based subscription, there is an inherent tradeoff between the privacy of the subscriber and the accuracy of the matching notifications. The former can be described in terms of how well the exposed information uniquely characterized the subscriber, and the latter how well the returned data items match the subscriber’s real interests. In this paper, we define the partial order based k-filter anonymity, which generalizes k-location anonymity by allowing the generalization of filters using the subsumption or covering relation. We then develop techniques for assessing and maintaining content anonymity in distributed broker-based content routing and publish/subscribe networks, namely filter generalization based on partial orders, a logically centralized broker for determining pairwise k-anonymous subscription paths, and optimizations for pairwise anonymity level detection.