A Consent Model for Blockchain-based Distributed Data Sharing Platforms

In modern healthcare systems, being able to share electronic health records is crucial for providing quality care and for enabling a larger spectrum of health services. Health data sharing is dependent on obtaining individual consent which, in turn, is hindered by a lack of resources. To this extend, blockchain-based platforms facilitate data sharing by inherently creating a trusted distributed network of users. These users are enabled to share their data without depending on the time and resources of specific players (such as the health services). In blockchain-based platforms, data governance mechanisms become very important due to the need to specify and monitor data sharing and data use conditions. In this paper, we present a blockchain-based data sharing consent model for access control over individual health data. We use smart contracts to dynamically represent the individual consent over health data and to enable data requesters to search and access them. The dynamic consent model extends upon two ontologies: the Data Use Ontology (DUO) which models the individual consent of users and the Automatable Discovery and Access Matrix (ADA-M) which describes queries from data requesters. We deploy the model on Ethereum blockchain and evaluate different data sharing scenarios. The contribution of this paper is to create an individual consent model for health data sharing platforms. Such a model guarantees that individual consent is respected and that there is accountability for all the participants in the data sharing platform. The evaluation of our solution indicates that such a data sharing model provides a flexible approach to decide how the data is used by data requesters. Our experimental evaluation shows that the proposed model is efficient and adapts to personalized access control policies in data sharing

User Incentives for Blockchain-based Data Sharing Platforms

Data sharing is very important for accelerating scientific research, business innovations, and for informing individuals. Yet, concerns over data privacy, cost, and lack of secure data-sharing solutions have prevented data owners from sharing data. To overcome these issues, several research works have proposed blockchain-based data-sharing solutions for their ability to add transparency and control to the data-sharing process. Yet, while models for decentralized data sharing exist, how to incentivize these structures to enable data sharing at scale remains largely unexplored. In this paper, we propose incentive mechanisms for decentralized data-sharing platforms. We use smart contracts to automate different payment options between data owners and data requesters. We discuss multiple cost pricing scenarios for data owners to monetize their data. Moreover, we simulate the incentive mechanisms on a blockchain-based data-sharing platform. The evaluation of our simulation indicates that a cost compensation model for the data owner can rapidly cover the cost of data sharing and balance the overall incentives for all the actors in the platform

An Agent Framework for Dynamic Health Data Aggregation for Research Purposes

This paper presents a model of a MAS framework for dynamic aggregation of population health data for research purposes. The contribution of the paper is twofold: First, it describes a MAS architecture that allows one to built on the fly anonymized databases from the distributed sources of data. Second, it shows how to improve the utility of the data with the growth of the database

A Multiagent System for Dynamic Data Aggregation in Medical Research

The collection of medical data for research purposes is a challenging and long-lasting process. In an effort to accelerate and facilitate this process we propose a new framework for dynamic aggregation of medical data from distributed sources. We use agent-based coordination between medical and research institutions. Our system employs principles of peer-to-peer network organization and coordination models to search over already constructed distributed databases and to identify the potential contributors when a new database has to be built. Our framework takes into account both the requirements of a research study and current data availability. This leads to better definition of database characteristics such as schema, content, and privacy parameters. We show that this approach enables a more efficient way to collect data for medical research

COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines

This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiological parameters of continuous monitoring, we can identify which patients have a higher risk of Metabolic Syndrome. We define temporal patterns for reasoning with continuous data and specify the coordination mechanisms for combining different sets of clinical guidelines that relate to this condition. The proposed solution is tested with data provided by twenty subjects, which used sensors for four days of continuous monitoring. The results are compared to the gold standard. The novelty of the framework stands in extending a temporal logic formalism, namely the Event Calculus, with temporal patterns. These patterns are helpful to specify the rules for reasoning with continuous data and in combining new knowledge into one consistent outcome that is tailored to the patient's profile. The overall approach opens new possibilities for delivering patient-tailored interventions and educational material before the patients present the symptoms of the disease

Agent Environments for Multi-agent Systems – A Research Roadmap

Ten years ago, researchers in multi-agent systems became more and more aware that agent systems consist of more than only agents. The series of workshops on Environments for Multi-Agent Systems (E4MAS 2004-2006) emerged from this awareness. One of the primary outcomes of this endeavor was a principled understanding that the agent environment should be considered as a primary design abstraction, equally important as the agents. A special issue in JAAMAS 2007 contributed a set of influential papers that define the role of agent environments, describe their engineering, and outline challenges in the field that have been the drivers for numerous follow up research efforts. The goal of this paper is to wrap up what has been achieved in the past 10 years and identify challenges for future research on agent environments. Instead of taking a broad perspective, we focus on three particularly relevant topics of modern software intensive systems: large scale, openness, and humans in the loop. For each topic, we reflect on the challenges outlined 10 years ago, present an example application that highlights the current trends, and from that outline challenges for the future. We conclude with a roadmap on how the different challenges could be tackled. © Springer International Publishing Switzerland 2015.Peer reviewe

MAGE: multi-agent game environment

We study the use of games as a metaphor for building social interaction in norm-governed multi-agent systems. As part of our research we propose MAGE (Multi-Agent Game Environment) as a logic-based framework that represents complex agent interactions as games. MAGE seeks to (a) reuse existing computational techniques for defining event-based normative sys- tem and (b) complement these techniques with a coordination component to support complex interactions. A game in MAGE is defined by a state, a set of normative rules describing the valid moves at different states and a set of effect rules describing how the state evolves as a result of a move taking place. Given a specification of the normative rules, in the implementation of a game, we use game containers as components that mediate the moves of players by checking their compliance with the rules of the game and by maintaining the state of the game. The reuse part of MAGE relates physical actions that happen in an agent environment to valid moves of a game representing the social environment of an application. MAGE allows to model complex interactions from simpler atomic sub-games. In this context, we investigate how coordination patterns can be used to dynamically play more than one game in parallel, change the status of games or choose amongst games. For this purpose, we examine how to define compound games from atomic sub-games. Compound games are build by describing the conditions and the patterns that activate a sub- game and the coordination mechanisms of MAGE ensure that sub-games are activated according to how interactions are specified to evolve at run-time. To illustrate the MAGE approach, we discuss how to use the framework to specify the social interaction in two different scenarios: (i) Open-Packet- World - a simple simulation where agents compete to collect and deliver packets in a grid and (ii) an earth-observation application - where agents represent services, both for clients and providers, and negotiate the provision of these services by combining argumentation and communication protocols. We also use the Open-Packet-World scenario to evaluate the effectiveness of the framework. We show that we can effectively support at run-time a large- scale multi-agent systems regulated by norms. We conclude the dissertation by summarising our contributions and identifying areas for future wor

MAGE: Multi-Agent Game Environment

We study the use of games as a metaphor for building social interaction in norm-governed multi-agent systems. As part of our research we propose MAGE (Multi-Agent Game Environment) as a logic-based framework that represents complex agent interactions as games. MAGE seeks to (a) reuse existing computational techniques for defining event-based normative sys- tem and (b) complement these techniques with a coordination component to support complex interactions. A game in MAGE is defined by a state, a set of normative rules describing the valid moves at different states and a set of effect rules describing how the state evolves as a result of a move taking place. Given a specification of the normative rules, in the implementation of a game, we use game containers as components that mediate the moves of players by checking their compliance with the rules of the game and by maintaining the state of the game. The reuse part of MAGE relates physical actions that happen in an agent environment to valid moves of a game representing the social environment of an application. MAGE allows to model complex interactions from simpler atomic sub-games. In this context, we investigate how coordination patterns can be used to dynamically play more than one game in parallel, change the status of games or choose amongst games. For this purpose, we examine how to define compound games from atomic sub-games. Compound games are build by describing the conditions and the patterns that activate a sub- game and the coordination mechanisms of MAGE ensure that sub-games are activated according to how interactions are specified to evolve at run-time. To illustrate the MAGE approach, we discuss how to use the framework to specify the social interaction in two different scenarios: (i) Open-Packet- World - a simple simulation where agents compete to collect and deliver packets in a grid and (ii) an earth-observation application - where agents represent services, both for clients and providers, and negotiate the provision of these services by combining argumentation and communication protocols. We also use the Open-Packet-World scenario to evaluate the effectiveness of the framework. We show that we can effectively support at run-time a large- scale multi-agent systems regulated by norms. We conclude the dissertation by summarising our contributions and identifying areas for future work

COMPOSE ::a model for composable computer interpretable guidelines using data from smart wearable systems

Motivating Scenario: The metabolic syndrome (MS) is a cluster of health conditions that occur together and increase the risk of heart disease, stroke and diabetes. As the availability of wearable sensors is becoming more popular, the collection of frequent physiological data from individuals has become easier than ever. This raises a need for new models that interpret continuous physiological values and provide meaningful interpretation for patients and caregivers. One way of interpreting these data is by automating existing evidence based guidelines. The assumption is that, by combining different clinical guidelines relating to the metabolic syndrome with the physiological data of the patient, we can predict deterioration states that may require medical attention. Such solution can assist caregivers in identifying high-risk patients and provide patient tailored interventions

Game-based e-retailing in GOLEM agent environments

We present a prototype multi-agent system whose goal is to support a 3d application for e-retailing. The prototype demonstrates how the use of agent environments can be amongst the most promising and flexible approaches to engineer e-retailing applications. We illustrate this point by showing how the agent environment golem supports social interactions and how it combines them with semantic-web technologies to develop the e-retailing application. We also describe the features of golem that allow a user to engage in e-retailing activities in order to explore the virtual social environment by searching and dynamically discovering new agents, products and services