Trust and Privacy in Development of Publish/Subscribe Systems

Publish/subscribe (pub/sub) is a widely deployed paradigm for information dissemination in a variety of distributed applications such as financial platforms, e-health frameworks and the Internet-of-Things. In essence, the pub/sub model considers one or more publishers generating feeds of information and a set of subscribers, the clients of the system. A pub/sub service is in charge of delivering the published information to interested clients. With the advent of cloud computing, we observe a growing tendency to externalize applications using pub/sub services to public clouds. This trend, despite its advantages, opens up multiple important data privacy and trust issues. Although multiple solutions for data protection have been proposed by the academic community, there is no unified view or framework describing how to deploy secure pub/sub systems on public clouds. To remediate this, we advocate towards a trust model which we believe can serve as basis for such deployments

Efficient and Confidentiality-Preserving Content-Based Publish/Subscribe with Prefiltering

Abstract: 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

Confidentiality-Preserving Publish/Subscribe: A Survey

Publish/subscribe (pub/sub) is an attractive communication paradigm for large-scale distributed applications running across multiple administrative domains. Pub/sub allows event-based information dissemination based on constraints on the nature of the data rather than on pre-established communication channels. It is a natural fit for deployment in untrusted environments such as public clouds linking applications across multiple sites. However, pub/sub in untrusted environments lead to major confidentiality concerns stemming from the content-centric nature of the communications. This survey classifies and analyzes different approaches to confidentiality preservation for pub/sub, from applications of trust and access control models to novel encryption techniques. It provides an overview of the current challenges posed by confidentiality concerns and points to future research directions in this promising field