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Barazzutti, Raphaël
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Barazzutti, Raphaël
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Voici les éléments 1 - 3 sur 3
- PublicationMétadonnées seulementInfrastructure Provisioning for Scalable Content-based Routing: Framework and Analysis(2012-1-13)
; ; ; ; ; 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. - PublicationMétadonnées seulementEfficient and Confidentiality-Preserving Content-Based Publish/Subscribe with Prefiltering(2015-6-25)
; ; ; ; 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. - PublicationMétadonnées seulementThrifty Privacy: Efficient Support for Privacy-Preserving Publish/Subscribe(: ACM, 2012-1-13)
; ; ; ; Content-based publish/subscribe is an appealing paradigm for building large-scale distributed applications. Such applications are often deployed over multiple administrative domains, some of which may not be trusted. Recent attacks in public clouds indicate that a major concern in untrusted domains is the enforcement of privacy. By routing data based on subscriptions evaluated on the content of publications, publish/subscribe systems can expose critical information to unauthorized parties. Information leakage can be avoided by the means of privacy-preserving filtering, which is supported by several mechanisms for encrypted matching. Unfortunately, all existing approaches have in common a high performance overhead and the difficulty to use classical optimization for content-based filtering such as per-attribute containment. In this paper, we propose a novel mechanism that greatly reduces the cost of supporting privacy-preserving filtering based on encrypted matching operators. It is based on a pre-filtering stage that can be combined with containment graphs, if available. Our experiments indicate that pre-filtering is able to significantly reduce the number of encrypted matching for a variety of workloads, and therefore the costs associated with the cryptographic mechanisms. Furthermore, our analysis shows that the additional data structures used for pre-filtering have very limited impact on the effectiveness of privacy preservation.