Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion

Function-specific schemes for verifiable computation

An integral component of modern computing is the ability to outsource data and computation to powerful remote servers, for instance, in the context of cloud computing or remote file storage. While participants can benefit from this interaction, a fundamental security issue that arises is that of integrity of computation: How can the end-user be certain that the result of a computation over the outsourced data has not been tampered with (not even by a compromised or adversarial server)? Cryptographic schemes for verifiable computation address this problem by accompanying each result with a proof that can be used to check the correctness of the performed computation. Recent advances in the field have led to the first implementations of schemes that can verify arbitrary computations. However, in practice the overhead of these general-purpose constructions remains prohibitive for most applications, with proof computation times (at the server) in the order of minutes or even hours for real-world problem instances. A different approach for designing such schemes targets specific types of computation and builds custom-made protocols, sacrificing generality for efficiency. An important representative of this function-specific approach is an authenticated data structure (ADS), where a specialized protocol is designed that supports query types associated with a particular outsourced dataset. This thesis presents three novel ADS constructions for the important query types of set operations, multi-dimensional range search, and pattern matching, and proves their security under cryptographic assumptions over bilinear groups. The scheme for set operations can support nested queries (e.g., two unions followed by an intersection of the results), extending previous works that only accommodate a single operation. The range search ADS provides an exponential (in the number of attributes in the dataset) asymptotic improvement from previous schemes for storage and computation costs. Finally, the pattern matching ADS supports text pattern and XML path queries with minimal cost, e.g., the overhead at the server is less than 4% compared to simply computing the result, for all our tested settings. The experimental evaluation of all three constructions shows significant improvements in proof-computation time over general-purpose schemes

SDN Access Control for the Masses

The evolution of Software-Defined Networking (SDN) has so far been predominantly geared towards defining and refining the abstractions on the forwarding and control planes. However, despite a maturing south-bound interface and a range of proposed network operating systems, the network management application layer is yet to be specified and standardized. It has currently poorly defined access control mechanisms that could be exposed to network applications. Available mechanisms allow only rudimentary control and lack procedures to partition resource access across multiple dimensions. We address this by extending the SDN north-bound interface to provide control over shared resources to key stakeholders of network infrastructure: network providers, operators and application developers. We introduce a taxonomy of SDN access models, describe a comprehensive design for SDN access control and implement the proposed solution as an extension of the ONOS network controller intent framework

Multi-tenant Pub/Sub processing for real-time data streams

Devices and sensors generate streams of data across a diversity of locations and protocols. That data usually reaches a central platform that is used to store and process the streams. Processing can be done in real time, with transformations and enrichment happening on-the-fly, but it can also happen after data is stored and organized in repositories. In the former case, stream processing technologies are required to operate on the data; in the latter batch analytics and queries are of common use. This paper introduces a runtime to dynamically construct data stream processing topologies based on user-supplied code. These dynamic topologies are built on-the-fly using a data subscription model defined by the applications that consume data. Each user-defined processing unit is called a Service Object. Every Service Object consumes input data streams and may produce output streams that others can consume. The subscription-based programing model enables multiple users to deploy their own data-processing services. The runtime does the dynamic forwarding of data and execution of Service Objects from different users. Data streams can originate in real-world devices or they can be the outputs of Service Objects. The runtime leverages Apache STORM for parallel data processing, that combined with dynamic user-code injection provides multi-tenant stream processing topologies. In this work we describe the runtime, its features and implementation details, as well as we include a performance evaluation of some of its core components.This work is partially supported by the European Research Council (ERC) un- der the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitivity (TIN2015-65316-P) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Permissionless Consensus

Blockchain protocols typically aspire to run in the permissionless setting, in which nodes are owned and operated by a large number of diverse and unknown entities, with each node free to start or stop running the protocol at any time. This setting is more difficult than the traditional permissioned setting, in which the set of nodes that will be running the protocol is fixed and known at the time of protocol deployment. The goal of this paper is to provide a model for reasoning about the rich design space of blockchain protocols and their capabilities and limitations in the permissionless setting. Our results demonstrate senses in which the permissionless setting is provably more challenging than its permissioned counterpart, and identify unavoidable protocol ingredients and additional assumptions that are necessary to achieve various forms of consensus in the permissionless setting.Comment: This is a journal version of the paper that subsumes earlier (conference) versions "Byzantine Generals in the Permissionless Setting" and "Resource Pools and the CAP Theorem

Practical Isolated Searchable Encryption in a Trusted Computing Environment

Cloud computing has become a standard computational paradigm due its numerous advantages, including high availability, elasticity, and ubiquity. Both individual users and companies are adopting more of its services, but not without loss of privacy and control. Outsourcing data and computations to a remote server implies trusting its owners, a problem many end-users are aware. Recent news have proven data stored on Cloud servers is susceptible to leaks from the provider, third-party attackers, or even from government surveillance programs, exposing users’ private data. Different approaches to tackle these problems have surfaced throughout the years. Naïve solutions involve storing data encrypted on the server, decrypting it only on the client-side. Yet, this imposes a high overhead on the client, rendering such schemes impractical. Searchable Symmetric Encryption (SSE) has emerged as a novel research topic in recent years, allowing efficient querying and updating over encrypted datastores in Cloud servers, while retaining privacy guarantees. Still, despite relevant recent advances, existing SSE schemes still make a critical trade-off between efficiency, security, and query expressiveness, thus limiting their adoption as a viable technology, particularly in large-scale scenarios. New technologies providing Isolated Execution Environments (IEEs) may help improve SSE literature. These technologies allow applications to be run remotely with privacy guarantees, in isolation from other, possibly privileged, processes inside the CPU, such as the operating system kernel. Prominent example technologies are Intel SGX and ARM TrustZone, which are being made available in today’s commodity CPUs. In this thesis we study these new trusted hardware technologies in depth, while exploring their application to the problem of searching over encrypted data, primarily focusing in SGX. In more detail, we study the application of IEEs in SSE schemes, improving their efficiency, security, and query expressiveness. We design, implement, and evaluate three new SSE schemes for different query types, namely Boolean queries over text, similarity queries over image datastores, and multimodal queries over text and images. These schemes can support queries combining different media formats simultaneously, envisaging applications such as privacy-enhanced medical diagnosis and management of electronic-healthcare records, or confidential photograph catalogues, running without the danger of privacy breaks in Cloud-based provisioned services