Content-based publish/subscribe provides a loosely-coupled and expressive form of communication for large-scale distributed systems. Confidentiality is a major challenge for publish/subscribe middleware deployed over multiple administrative domains. Encrypted matching allows confidentiality-preserving content-based filtering but has high performance overheads. It may also prevent the use of classical optimizations based on subscriptions containment. We propose a support mechanism that reduces the cost of encrypted matching, in the form of a prefiltering operator using Bloom filters and simple randomization techniques. This operator greatly reduces the amount of encrypted subscriptions that must be matched against incoming encrypted publications. It leverages subscription containment information when available, but also ensures that containment confidentiality is preserved otherwise. We propose containment obfuscation techniques and provide a rigorous security analysis of the information leaked by Bloom filters in this case. We conduct a thorough experimental evaluation of prefiltering under a large variety of workloads. Our results indicate that prefiltering is successful at reducing the space of subscriptions to be tested in all cases. We show that while there is a tradeoff between prefiltering efficiency and information leakage when using containment obfuscation, it is practically possible to obtain good prefiltering performance while securing the technique against potential leakages.

Publish/subscribe (pub/sub) infrastructures running as a service on cloud environments offer simplicity and flexibility for composing distributed applications. Provisioning them appropriately is however challenging. The amount of stored subscriptions and incoming publications varies over time, and the computational cost depends on the nature of the applications and in particular on the filtering operation they require (e.g., content-based vs. topic-based, encrypted vs. non-encrypted filtering). The ability to elastically adapt the amount of resources required to sustain given throughput and delay requirements is key to achieving cost-effectiveness for a pub/sub service running in a cloud environment. In this paper, we present the design and evaluation of an elastic content-based pub/sub system: E-STREAMHUB. Specific contributions of this paper include: (1) a mechanism for dynamic scaling, both out and in, of stateful and stateless pub/sub operators, (2) a local and global elasticity policy enforcer maintaining high system utilization and stable end-to-end latencies, and (3) an evaluation using real-world tick workload from the Frankfurt Stock Exchange and encrypted content-based filtering.

Content-based publish/subscribe is an attractive paradigm for designing large-scale systems, as it decouples producers of information from consumers. This provides extensive flexibility for applications, which can use a modular architecture. Using this architecture, each participant expresses its interest in events by means of filters on the content of those events instead of using pre-established communication channels. However, matching events against filters has a non-negligible processing cost. Scaling the infrastructure with the number of users or events requires appropriate provisioning of resources for each of the operations involved: routing and filtering. In this paper, we propose and describe a generic, modular, and scalable infrastructure for supporting high-performance content-based publish/subscribe. We analyze its properties and show how it dynamically scales in a realistic setting. Our results provide valuable insights into the design and deployment of scalable content-based routing infrastructures.