The paper “Improving Data Sharing in Data Rich Environments” was accepted at the IEEE Big Data International Workshop on Policy-based Autonomic Data Governance (PADG), part of the 15th IEEE International Conference on Big Data (Big Data 2017), December 11-14, 2017, Boston, MA, USA. This work was done in collaboration with our partners (BAE Systems, IBM UK and IBM US) from the DAIS International Technology Alliance (ITA). The paper can be found here.
Abstract: The increasing use of big data comes along with the problem of ensuring correct and secure data access. There is a need to maximise the data dissemination whilst controlling their access. Depending on the type of users different qualities and parts of data are shared. We introduce an alteration mechanism, more precisely a restriction one, based on a policy analysis language. The alteration reflects the level of trust and relations the users have, and are represented as policies inside the data sharing agreements. These agreements are attached to the data and are enforced every time the data are accessed, used or shared. We show the use of our alteration mechanism with a military use case, where different parties are involved during the missions, and they have different relations of trust and partnership.
The work was supported by EPSRC Project CIPART grant no. EP/L022729/1 and DAIS ITA (Sponsored by U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001).
Abstract: Data-intensive environments enable us to capture information and knowledge about the physical surroundings, to optimise our resources, enjoy personalised services and gain unprecedented insights into our lives. However, to obtain these endeavours extracted from the data, this data should be generated, collected and the insight should be exploited. Following an argumentation reasoning approach for data processing and building on the theoretical background of data management, we highlight the importance of data sharing agreements (DSAs) and quality attributes for the proposed data processing mechanism. The proposed approach is taking into account the DSAs and usage policies as well as the quality attributes of the data, which were previously neglected compared to existing methods in the data processing and management field. Previous research provided techniques towards this direction; however, a more intensive research approach for processing techniques should be introduced for the future to enhance the value creation from the data and new strategies should be formed around this data generated daily from various devices and sources.
This work was supported by FP7 EU-funded project Coco Cloud grant no.: 610853, and EPSRC Project CIPART grant no. EP/L022729/1.
Abstract: Internet of Things environments enable us to capture more and more data about the physical environment we live in and about ourselves. The data enable us to optimise resources, personalise services and offer unprecedented insights into our lives. However, to achieve these insights data need to be shared (and sometimes sold) between organisations imposing rights and obligations upon the sharing parties and in accordance with multiple layers of sometimes conflicting legislation at international, national and organisational levels. In this work, we show how such rules can be captured in a formal representation called “Data Sharing Agreements”. We introduce the use of abductive reasoning and argumentation based techniques to detect inconsistencies in the rules applicable and resolve them by assigning priorities to the rules. We show how through the use of argumentation based techniques use-cases taken from real life application are handled flexibly addressing trade-offs between confidentiality, privacy, availability and safety.
Cloud and mobile are two major computing paradigms that are rapidly converging. However, these models still lack a way to manage the dissemination and control of personal and business-related data. To this end, we propose a framework to control the sharing, dissemination and usage of data based on mutually agreed Data Sharing Agreements (DSAs). These agreements are enforced uniformly, and end-to-end, both on Cloud and mobile platforms, and may reflect legal, contractual or user-defined preferences. We introduce an abstraction layer that makes available the enforcement functionality across different types of nodes whilst hiding the distribution of components and platform specifics. We also discuss a set of different types of nodes that may run such a layer.
FP7, Partners: Hewlett-Packard, The Italian National Research Council, Imperial College London, University of Oslo, SAP, Atos, AGID, Bird & Bird, and Grupo Hospitalario Quirón.
The project aims to facilitate data sharing in cloud environments by providing end-to-end data centric security from the client to the cloud based on the (semi-)automated enforcement ofData Sharing Agreements. These agreements may reflect legal, contractual or user defined preferences, which may be conflicting and thus an appropriate balance and model for their enforcement must be found.
Secure dissemination of data in crisis management scenarios is always difficult to achieve because network connectivity is intermittent or absent. In this work we have combined data-centric information protection techniques based on usage control, sticky policies and rights management with opportunistic networking to enable the dissemination of information between first responders in crisis management situations. The dissemination of keys for access to the information is controlled by a policy hierarchy that describes the permitted devolution of control. Policies are evaluated whenever two users are in proximity in the field and keys are distributed upon successful evaluation. Simulations with conservative mobility models show that the delay on information access i.e., the difference between the distribution of information and the distribution of keys remains small for realistic densities of users in the geographical areas.