Modelling, validating, and ranking of secure service compositions

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn the world of large-scale applications, software as a service (SaaS) in general and use of microservices, in particular, is bringing service-oriented architectures to a new level: Systems in general and systems that interact with human users (eg, sociotechnical systems) in particular are built by composing microservices that are developed independently and operated by different parties. At the same time, SaaS applications are used more and more widely by enterprises as well as public services for providing critical services, including those processing security or privacy of relevant data. Therefore, providing secure and reliable service compositions is increasingly needed to ensure the success of SaaS solutions. Building such service compositions securely is still an unsolved problem. In this paper, we present a framework for modelling, validating, and ranking secure service compositions that integrate both automated services as well as services that interact with humans. As a unique feature, our approach for ranking services integrates validated properties (eg, based on the result of formally analysing the source code of a service implementation) as well as contractual properties that are part of the service level agreement and, thus, not necessarily ensured on a technical level

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

Application of Risk Metrics for Role Mining

Incorporating risk consideration in access control systems has recently become a popular research topic. Related to this is risk awareness which is needed to enable access control in an agile and dynamic way. While risk awareness is probably known for an established access control system, being aware of risk even before the access control system is defined can mean identification of users and permissions that are most likely to lead to dangerous or error-prone situations from an administration point of view. Having this information available during the role engineering phase allows data analysts and role engineers to highlight potentially risky users and permissions likely to be misused. While there has been much recent work on role mining, there has been little consideration of risk during the process. In this thesis, we propose to add risk awareness to role mining. We aggregate the various possible risk factors and categorize them into four general types, which we refer to as risk metrics, in the context of role mining. Next, we propose a framework that incorporates some specific examples of each of these risk metrics before and after role mining. We have implemented a proof-of-concept prototype, a Risk Awareness system for Role Mining (aRARM) based on this framework and applied it to two case studies: a small organizational project and a university database setting. The aRARM prototype is automatically able to detect different types of risk factors when we add different types of noise to this data. The results from the two case studies draw some correlation between the behavior of the different risk factors due to different types and amounts of noise. We also discuss the effect of the different types and amounts of noise on the different role mining algorithms implemented for this study. While the detection rating value for calculating the risk priority number has previously been calculated after role mining, we attempt to find an initial estimate of the detection rating before role mining

CamFlow: Managed Data-sharing for Cloud Services

A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS applications. From the start, strong isolation between cloud tenants was seen to be of paramount importance, provided first by virtual machines (VM) and later by containers, which share the operating system (OS) kernel. Increasingly it is the case that applications also require facilities to effect isolation and protection of data managed by those applications. They also require flexible data sharing with other applications, often across the traditional cloud-isolation boundaries; for example, when government provides many related services for its citizens on a common platform. Similar considerations apply to the end-users of applications. But in particular, the incorporation of cloud services within `Internet of Things' architectures is driving the requirements for both protection and cross-application data sharing. These concerns relate to the management of data. Traditional access control is application and principal/role specific, applied at policy enforcement points, after which there is no subsequent control over where data flows; a crucial issue once data has left its owner's control by cloud-hosted applications and within cloud-services. Information Flow Control (IFC), in addition, offers system-wide, end-to-end, flow control based on the properties of the data. We discuss the potential of cloud-deployed IFC for enforcing owners' dataflow policy with regard to protection and sharing, as well as safeguarding against malicious or buggy software. In addition, the audit log associated with IFC provides transparency, giving configurable system-wide visibility over data flows. [...]Comment: 14 pages, 8 figure

An Access Control Model to Facilitate Healthcare Information Access in Context of Team Collaboration

The delivery of healthcare relies on the sharing of patients information among a group of healthcare professionals (so-called multidisciplinary teams (MDTs)). At present, electronic health records (EHRs) are widely utilized system to create, manage and share patient healthcare information among MDTs. While it is necessary to provide healthcare professionals with privileges to access patient health information, providing too many privileges may backfire when healthcare professionals accidentally or intentionally abuse their privileges. Hence, finding a middle ground, where the necessary privileges are provided and malicious usage are avoided, is necessary. This thesis highlights the access control matters in collaborative healthcare domain. Focus is mainly on the collaborative activities that are best accomplished by organized MDTs within or among healthcare organizations with an objective of accomplishing a specific task (patient treatment). Initially, we investigate the importance and challenges of effective MDTs treatment, the sharing of patient healthcare records in healthcare delivery, patient data confidentiality and the need for flexible access of the MDTs corresponding to the requirements to fulfill their duties. Also, we discuss access control requirements in the collaborative environment with respect to EHRs and usage scenario of MDTs collaboration. Additionally, we provide summary of existing access control models along with their pros and cons pertaining to collaborative health systems. Second, we present a detailed description of the proposed access control model. In this model, the MDTs is classified based on Belbin’s team role theory to ensure that privileges are provided to the actual needs of healthcare professionals and to guarantee confidentiality as well as protect the privacy of sensitive patient information. Finally, evaluation indicates that our access control model has a number of advantages including flexibility in terms of permission management, since roles and team roles can be updated without updating privilege for every user. Moreover, the level of fine-grained control of access to patient EHRs that can be authorized to healthcare providers is managed and controlled based on the job required to meet the minimum necessary standard and need-to-know principle. Additionally, the model does not add significant administrative and performance overhead.publishedVersio

IaaS-cloud security enhancement: an intelligent attribute-based access control model and implementation

The cloud computing paradigm introduces an efficient utilisation of huge computing resources by multiple users with minimal expense and deployment effort compared to traditional computing facilities. Although cloud computing has incredible benefits, some governments and enterprises remain hesitant to transfer their computing technology to the cloud as a consequence of the associated security challenges. Security is, therefore, a significant factor in cloud computing adoption. Cloud services consist of three layers: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud computing services are accessed through network connections and utilised by multi-users who can share the resources through virtualisation technology. Accordingly, an efficient access control system is crucial to prevent unauthorised access. This thesis mainly investigates the IaaS security enhancement from an access control point of view. [Continues.

Obstructions in Security-Aware Business Processes

This Open Access book explores the dilemma-like stalemate between security and regulatory compliance in business processes on the one hand and business continuity and governance on the other. The growing number of regulations, e.g., on information security, data protection, or privacy, implemented in increasingly digitized businesses can have an obstructive effect on the automated execution of business processes. Such security-related obstructions can particularly occur when an access control-based implementation of regulations blocks the execution of business processes. By handling obstructions, security in business processes is supposed to be improved. For this, the book presents a framework that allows the comprehensive analysis, detection, and handling of obstructions in a security-sensitive way. Thereby, methods based on common organizational security policies, process models, and logs are proposed. The Petri net-based modeling and related semantic and language-based research, as well as the analysis of event data and machine learning methods finally lead to the development of algorithms and experiments that can detect and resolve obstructions and are reproducible with the provided software