Hugues Mercier

Thrifty privacy: efficient support for privacy-preserving publish/subscribe

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.

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 leads 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.

EpTO: An Epidemic Total Order Algorithm for Large-Scale Distributed Systems

The ordering of events is a fundamental problem of distributed computing and has been extensively studied over several decades. From all the available orderings, total ordering is of particular interest as it provides a powerful abstraction for building reliable distributed applications. Unfortunately, deterministic total order algorithms scale poorly and are therefore unfit for modern large-scale applications. The main contribution of this paper is EpTO, a total order algorithm with probabilistic agreement that scales both in the number of processes and events. EpTO provides deterministic safety and probabilistic liveness: integrity, total order and validity are always preserved, while agreement is achieved with arbitrarily high probability. We show that EpTO is well-suited for large-scale dynamic distributed systems: it does not require a global clock nor synchronized processes, and it is highly robust even when the network suffers from large delays and significant churn and message loss.

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

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.

A Performance Evaluation of Erasure Coding Libraries for Cloud-Based Data Stores

Erasure codes have been widely used over the last decade to implement reliable data stores. They offer interesting trade-offs between efficiency, reliability, and storage overhead. Indeed, a distributed data store holding encoded data blocks can tolerate the failure of multiple nodes while requiring only a fraction of the space necessary for plain replication, albeit at an increased encoding and decoding cost. There exists nowadays a number of libraries implementing several variations of erasure codes, which notably differ in terms of complexity and implementation-specific optimizations. Seven years ago, Plank et al.i¾ź[14] have conducted a comprehensive performance evaluation of open-source erasure coding libraries available at the time to compare their raw performance and measure the impact of different parameter configurations. In the present experimental study, we take a fresh perspective at the state of the art of erasure coding libraries. Not only do we cover a wider set of libraries running on modern hardware, but we also consider their efficiency when used in realistic settings for cloud-based storage, namely when deployed across several nodes in a data centre. Our measurements therefore account for the end-to-end costs of data accesses over several distributed nodes, including the encoding and decoding costs, and shed light on the performance one can expect from the various libraries when deployed in a real system. Our results reveal important differences in the efficiency of the different libraries, notably due to the type of coding algorithm and the use of hardware-specific optimizations.

Efficient Key Updates through Subscription Re-encryption for Privacy-Preserving Publish/Subscribe

Content-based publish/subscribe (pub/sub) is an appealing information dissemination paradigm for distributed systems. Consumers of data subscribe to a pub/sub service, typically offered through a distributed broker overlay, and indicate their interests as constraints over the information content. Publishers generate the information flow, which the brokers filter and route to the interested subscribers. Protecting the information confidentiality, and in particular the interests of subscribers, is an important concern when brokers are located in untrusted domains such as public clouds. Encrypted matching techniques allow untrusted brokers to store encrypted subscriptions and match them against encrypted publications. Updates of encryption keys regularly happen in such contexts due to changes in trust relations. These key updates cause the invalidation of stored encrypted subscriptions and force subscribers to re-encrypt and re-submit them. This long and costly operation impacts the pub/sub service continuity and performance. In this paper, we propose a novel technique that allows updating encrypted subscriptions directly at the brokers while maintaining privacy. We present an implementation of the technique for the ASPE encrypted matching scheme and prove the security of our extension. We evaluate its practical effectiveness through a prototype implementation including a dependable key distribution protocol. Our experiments show the ability to handle key updates while preserving service continuity and performance.

On the Cost of Safe Storage for Public Clouds: An Experimental Evaluation

Cloud-based storage services such as Dropbox, Google Drive and OneDrive are increasingly popular for storing enterprise data, and they have already become the de facto choice for cloud-based backup of hundreds of millions of regular users. Drawn by the wide range of services they provide, no upfront costs and 24/7 availability across all personal devices, customers are well-aware of the benefits that these solutions can bring. However, most users tend to forget—or worse ignore—some of the main drawbacks of such cloud-based services, namely in terms of privacy. Data entrusted to these providers can be leaked by hackers, disclosed upon request from a governmental agencys subpoena, or even accessed directly by the storage providers (e.g., for commercial benefits). While there exist solutions to prevent or alleviate these problems, they typically require direct intervention from the clients, like encrypting their data before storing it, and reduce the benefits provided such as easily sharing data between users. This practical experience report studies a wide range of security mechanisms that can be used atop standard cloud-based storage services. We present the details of our evaluation testbed and discuss the design choices that have driven its implementation. We evaluate several state-of-the-art techniques with varying security guarantees responding to user-assigned security and privacy criteria. Our results reveal the various trade-offs of the different techniques by means of representative workloads on top of industry-grade storage services.

Infrastructure Provisioning for Scalable Content-Based Routing: Framework and Analysis

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.

Worst-case, information and all-blocks locality in distributed storage systems: an explicit comparison

Distributed storage systems often use erasure coding techniques to provide reliability while decreasing the storage overhead required by replication. Due to the drawbacks of standard MDS erasure-correcting codes, numerous coding schemes recently proposed for distributed storage systems target other metrics such as repair locality and repair bandwidth. Unfortunately, these schemes are not always practical, and for most of them locality covers information data only. In this article, we compare three explicit linear codes for three types of locality: a Reed-Solomon code for worst-case locality, a recently proposed pyramid code for information locality and the Hamming code HAM, an optimal locally repairable code directly built from its generator matrix for all-blocks locality. We also provide an efficient way for repairing HAM and show that for the same level of storage overhead HAM provides faster encoding, faster repair and lower repair bandwidth than the other two solutions while requiring less than fifty lines of code.

Have a Seat on the ErasureBench: Easy Evaluation of Erasure Coding Libraries for Distributed Storage Systems

We present ErasureBench, an open-source framework to test and benchmark erasure coding implementations for distributed storage systems under realistic conditions. ErasureBench automatically instantiates and scales a cluster of storage nodes, and can seamlessly leverage existing failure traces. As a first example, we use ErasureBench to compare three coding implementations: a (10,4) Reed-Solomon (RS) code, a (10,6,5) locally repairable code (LRC), and a partition of the data source in ten pieces without error-correction. Our experiments show that LRC and RS codes require the same repair throughput when used with small storage nodes, since cluster and network management traffic dominate at this regime. With large storage nodes, read and write traffic increases and our experiments confirm the theoretical and practical tradeoffs between the storage overhead and repair bandwidth of RS and LRC codes.