On Anonymizing the Provenance of Collection-Based Workflows

We examine in this paper the problem of anonymizing the prove-nance of collection-oriented workflows, in which the constituent modules use and generate sets of data records. Despite their popularity , this kind of workflow has been overlooked in the literature w.r.t privacy. We, therefore, set out in this paper to examine the following questions: How the provenance of a collection-based module can be anonymized? Can lineage information be preserved? Beyond a single module, how can the provenance of a whole work-flow be anonymized? As well as addressing the above questions, we report on evaluation exercises that assess the effectiveness and efficiency of our solution. In particular, we tease apart the parameters that impact the quality of the obtained anonymized provenance information

Insider Threats in Emerging Mobility-as-a-Service Scenarios

Mobility as a Service (MaaS) applies the everything-as- \ a-service paradigm of Cloud Computing to transportation: a MaaS \ provider offers to its users the dynamic composition of solutions of \ different travel agencies into a single, consistent interface. \ Traditionally, transits and data on mobility belong to a scattered \ plethora of operators. Thus, we argue that the economic model of \ MaaS is that of federations of providers, each trading its resources to \ coordinate multi-modal solutions for mobility. Such flexibility comes \ with many security and privacy concerns, of which insider threat is \ one of the most prominent. In this paper, we follow a tiered structure \ — from individual operators to markets of federated MaaS providers \ — to classify the potential threats of each tier and propose the \ appropriate countermeasures, in an effort to mitigate the problems

Time, Money and Effort: A Practical Approach to Digital Content Management

As libraries and archives continue to convert mass quantities of collections to digital form, we are faced with ensuring long term accessibility to these digital assets. This article addresses the process one institution undertook to evaluate the digital content management and preservation landscape to plan for future growth and expansion of its digital program

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met