A Privacy-Aware Access Control Model for Distributed Network Monitoring

International audienceIn this paper, we introduce a new access control model that aims at addressing the privacy implications surrounding network monitoring. In fact, despite its importance, network monitoring is natively leakage-prone and, moreover, this is exacerbated due to the complexity of the highly dynamic monitoring procedures and infrastructures, that may include multiple traffic observation points, distributed mitigation mechanisms and even inter-operator cooperation. Conceived on the basis of data protection legislation, the proposed approach is grounded on a rich in expressiveness information model, that captures all the underlying monitoring concepts along with their associations. The model enables the specification of contextual authorisation policies and expressive separation and binding of duty constraints. Finally, two key innovations of our work consist in the ability to define access control rules at any level of abstraction and in enabling a verification procedure, which results in inherently privacy-aware workflows, thus fostering the realisation of the Privacy by Design vision

Multiuser Museum Interactives for Shared Cultural Experiences: an Agent-based Approach

Multiuser museum interactives are computer systems installed in museums or galleries which allow several visitors to interact together with digital representations of artefacts and information from the museum's collection. WeCurate is such a system, providing a multiuser curation workflow where the aim is for the users to synchronously view and discuss a selection of images, finally choosing a subset of these images that the group would like to add to their group collection. The system presents two main problems: workflow control and group decision making. An Electronic Institution (EI) is used to model the workflow into scenes, where users engage in specific activities in specific scenes. A multiagent system is used to support group decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e.g. aggregating opinions, proposing interactions and resolutions between disagreeing group members and choosing images for discussion

Locality-aware scientific workflow engine for fast-evolving spatiotemporal sensor data, A

2017 Spring.Includes bibliographical references.Discerning knowledge from voluminous data involves a series of data manipulation steps. Scientists typically compose and execute workflows for these steps using scientific workflow management systems (SWfMSs). SWfMSs have been developed for several research communities including but not limited to bioinformatics, biology, astronomy, computational science, and physics. Parallel execution of workflows has been widely employed in SWfMSs by exploiting the storage and computing resources of grid and cloud services. However, none of these systems have been tailored for the needs of spatiotemporal analytics on real-time sensor data with high arrival rates. This thesis demonstrates the development and evaluation of a target-oriented workflow model that enables a user to specify dependencies among the workflow components, including data availability. The underlying spatiotemporal data dispersion and indexing scheme provides fast data search and retrieval to plan and execute computations comprising the workflow. This work includes a scheduling algorithm that targets minimizing data movement across machines while ensuring fair and efficient resource allocation among multiple users. The study includes empirical evaluations performed on the Google cloud

Engineering multiuser museum interactives for shared cultural experiences

Multiuser museum interactives are computer systems installed in museums or galleries which allow several visitors to interact together with digital representations of artefacts and information from the museum׳s collection. In this paper, we describe WeCurate, a socio-technical system that supports co-browsing across multiple devices and enables groups of users to collaboratively curate a collection of images, through negotiation, collective decision making and voting. The engineering of such a system is challenging since it requires to address several problems such as: distributed workflow control, collective decision making and multiuser synchronous interactions. The system uses a peer-to-peer Electronic Institution (EI) to manage and execute a distributed curation workflow and models community interactions into scenes, where users engage in different social activities. Social interactions are enacted by intelligent agents that interface the users participating in the curation workflow with the EI infrastructure. The multiagent system supports collective decision making, representing the actions of the users within the EI, where the agents advocate and support the desires of their users e.g. aggregating opinions for deciding which images are interesting enough to be discussed, and proposing interactions and resolutions between disagreeing group members. Throughout the paper, we describe the enabling technologies of WeCurate, the peer-to-peer EI infrastructure, the agent collective decision making capabilities and the multi-modal interface. We present a system evaluation based on data collected from cultural exhibitions in which WeCurate was used as supporting multiuser interactive

Generic processing of real-time physiological data in the cloud

There is an emerging market in the collection of physiological data for analysis and presentation to end-users via web technologies for applications including health and fitness, telemedicine and self-tracking. As technology has improved, real-time streaming of physiological data, providing end-to-end user feedback has become feasible, allowing for innovative applications to be developed. Currently, there is no standardised method of collecting physiological data over the web for analysis and feedback to an end-user in real-time; existing platforms only support specific devices and application domains. This paper proposes a generic methodology and architecture for the collection, analysis and presentation of physiological data. It defines a standard method of encapsulating data from heterogeneous sensors, performing transformations on it and analysing it. The approach is evaluated through an implementation of the architecture using cloud computing technologies and an appropriate case study

Preserving Communication Context. Virtual workspace and interpersonal space in Japanese CSCW.

The past decade has seen the development of a perspective\ud holding that technology is socially constructed (Mackenzie and Wacjman, 1985; Bijker, Hughes and Pinch, 1987; Bijker and Law, 1992). This paper examines the social construction of one group of technologies, systems for computer supported cooperative work (CSCW). It describes the design of CSCW in Japan, with particular attention to the influence of culture on the design process. Two case studies are presented to illustrate the argument that culture is an important factor in technology design, despite commonly held assumptions about the neutrality and objectivity of science and technology. The paper further argues that, by looking at\ud CSCW systems as texts which reflect the context of their production and the society from which they come, we may be better able to understand the transformations that operate when these texts are “read” in the contexts of their implementation

Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration

Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G.Comment: 8 pages, 4 figures, 1 tabl