Semantic Security for E-Health: A Case Study in Enhanced Access Control

Data collection, access and usage are essential for many forms of collaborative research. E-Health represents one area with much to gain by sharing of data across organisational boundaries. In such contexts, security and access control are essential to protect the often complex, privacy and information governance concerns of associated stakeholders. In this paper we argue that semantic technologies have unique benefits for specification and enforcement of security policies that cross organisation boundaries. We illustrate this through a case study based around the International Niemann-Pick Disease (NPD) Registry (www.inpdr.org) - which typifies many current e-Health security processes and policies. We show how approaches based upon ontology-based policy specification overcome many of the current security challenges facing the development of such systems and enhance access control by leveraging existing security information associated with clinical collaborators

Semantic-Based Privacy Protection of Electronic Health Records for Collaborative Research

Combined health information and web-based technologies can be used to support healthcare and research activities associated with electronic health records (EHRs). EHRs used for research purposes demand privacy, confidentiality and all information governance concerns are addressed. However, existing solutions are unable to meet the evolving research needs especially when supporting data access and linkage across organization boundaries. In this work, we show how semantic methods can aid in the specification and enforcement of policies for privacy protection. This is illustrated through a case study associated with the Australasian Diabetes Data Network (ADDN), the national paediatric type-1 diabetes data registry and the Australian Urban Research Infrastructure Network (AURIN) platform that supports Australia-wide access to urban and built environment data sets. Specifically we show that through extending the eXtensible Access Control Markup Language (XACML) with semantic capabilities, we are able to support fine-grained privacy-preserving policies leveraging semantic reasoning that is not directly available in XACML or other existing security policy specification languages

Experiences modelling and using object-oriented telecommunication service frameworks in SDL

This paper describes experiences in using SDL and its associated tools to create telecommunication services by producing and specialising object-oriented frameworks. The chosen approach recognises the need for the rapid creation of validated telecommunication services. It introduces two stages to service creation. Firstly a software expert produces a service framework, and secondly a telecommunications ‘business consultant' specialises the framework by means of graphical tools to rapidly produce services. Here the focus is given to the underlying technology required. In particular, the advantages and disadvantages of SDL and tools for this purpose are highlighted

A Semantic-Based K-Anonymity Scheme for Health Record Linkage

Record linkage is a technique for integrating data from sources or providers where direct access to the data is not possible due to security and privacy considerations. This is a very common scenario for medical data, as patient privacy is a significant concern. To avoid privacy leakage, researchers have adopted k-anonymity to protect raw data from re-identification however they cannot avoid associated information loss, e.g. due to generalisation. Given that individual-level data is often not disclosed in the linkage cases, but yet remains potentially re-discoverable, we propose semantic-based linkage k-anonymity to de-identify record linkage with fewer generalisations and eliminate inference disclosure through semantic reasoning

Semantic-Based Policy Composition for Privacy-Demanding Data Linkage

Record linkage can be used to support current and future health research across populations however such approaches give rise to many challenges related to patient privacy and confidentiality including inference attacks. To address this, we present a semantic-based policy framework where linkage privacy detects attribute associations that can lead to inference disclosure issues. To illustrate the effectiveness of the approach, we present a case study exploring health data combining spatial, ethnicity and language information from several major on-going projects occurring across Australia. Compared with classic access control models, the results show that our proposal outperforms other approaches with regards to effectiveness, reliability and subsequent data utility

Privacy-Preserving Access Control in Electronic Health Record Linkage

Sharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. However traditional methods such as k-anonymity and its derivations are often over-generalizing resulting in lower data accuracy. To tackle this issue, we present the Semantic Linkage K-Anonymity (SLKA) approach supporting ongoing record linkages. We show how SLKA balances privacy and utility preservation through detecting risky combinations hidden in data releases

Fake News Detection Through Graph-based Neural Networks: A Survey

The popularity of online social networks has enabled rapid dissemination of information. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information can also spread rapidly. This can lead to considerable and negative impacts on society. Identifying, labelling and debunking online misinformation as early as possible has become an increasingly urgent problem. Many methods have been proposed to detect fake news including many deep learning and graph-based approaches. In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news. In this paper, we present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques. We classify existing graph-based methods into knowledge-driven methods, propagation-based methods, and heterogeneous social context-based methods, depending on how a graph structure is constructed to model news related information flows. We further discuss the challenges and open problems in graph-based fake news detection and identify future research directions.Comment: 18 pages, 3 tables, 7 figure

An Energy-aware, Fault-tolerant, and Robust Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown environmental factors. Secondly, autonomous vehicles can have failures or hardware constraints, such as limited battery life. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on model-free, deep multi-agent reinforcement learning. In this approach, the agents are trained to automatically recharge themselves when required, to support continuous collective patrolling. A distributed homogeneous multi-agent architecture is proposed, where all patrolling agents execute identical policies locally based on their local observations and shared information. This architecture provides a fault-tolerant and robust patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. The solution is validated through simulation experiments from multiple perspectives, including the overall patrol performance, the efficiency of battery recharging strategies, and the overall fault tolerance and robustness