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Pasquier, Thomas

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Pasquier

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Thomas

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Pasquier, Thomas

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Now showing 1 - 5 of 5
  • Publication

    Twenty Security Considerations for Cloud-Supported Internet of Things

    (Institute of Electrical and Electronics Engineers (IEEE), 2016) Singh, Jatinder; Pasquier, Thomas; Bacon, Jean; Ko, Ronny; Eyers, David

    To realise the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends towards the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy and personal safety risks that arise beyond these subsystems; that is, from the wide-scale, cross- platform openness that cloud services bring to IoT. In this paper we focus on security considerations for IoT from the perspectives of cloud tenants, end-users and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyse the current state of cloud-supported IoT to make explicit the security considerations that require further work. Keywords—Internet of Things, Cloud, Security, Privacy, Data

  • Publication

    FlowK: Information Flow Control for the Cloud

    (2018-06-20) Pasquier, Thomas; Bacon, Jean; Eyers, David

    Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions and although a wealth of law and regulation has emerged, the technical basis for enforcing and demonstrating compliance lags behind. Our CloudSafetyNet project aims to show that Information Flow Control (IFC) can augment existing security mechanisms and provide continuous enforcement of extended. finer-grained application-level security policy in the cloud. We present FlowK, a loadable kernel module for Linux, as part of a proof of concept that IFC can be provided for cloud computing. Following the principle of policy-mechanism separation, IFC policy is assumed to be expressed at application level and FlowK provides mechanisms to enforce IFC policy at runtime. FlowK’s design minimises the changes required to existing software when IFC is provided. To show how FlowK can be integrated with cloud software we have designed and evaluated a framework for deploying IFC-aware web applications, suitable for use in a PaaS cloud.

  • Publication

    FlowR: Aspect Oriented Programming for Information Flow Control in Ruby

    (ACM, 2014) Pasquier, Thomas; Bacon, Jean; Shand, Brian

    This paper reports on our experience with providing Information Flow Control (IFC) as a library. Our aim was to support the use of an unmodified Platform as a Service (PaaS) cloud infrastructure by IFC-aware web applications. We discuss how Aspect Oriented Programming (AOP) overcomes the limitations of RubyTrack, our first approach. Although use of AOP has been mentioned as a possibility in past IFC literature we believe this paper to be the first illustration of how such an implementation can be attempted. We discuss how we built FlowR (Information Flow Control for Ruby), a library extending Ruby to provide IFC primitives using AOP via the Aquarium open source library. Previous attempts at providing IFC as a language extension required either modification of an interpreter or significant code rewriting. FlowR provides a strong separation between functional implementation and security constraints which supports easier development and maintenance; we illustrate with practical examples. In addition, we provide new primitives to describe IFC constraints on objects, classes and meth- ods that, to our knowledge, are not present in related work and take full advantage of an object oriented language (OO language). The experience reported here makes us confident that the tech- niques we use for Ruby can be applied to provide IFC for any Ob- ject Oriented Program (OOP) whose implementation language has an AOP library. D.2.2 [Software Engineer- ing]: Design Tools and Techniques; D.2.4 [Software Engineer- ing]: Software/Program Verification Keywords Information Flow Control, Aspect Oriented Program- ming, Security

  • Publication

    Camflow: Managed Data-Sharing for Cloud Services

    (Institute of Electrical and Electronics Engineers (IEEE), 2017) Pasquier, Thomas; Singh, Jatinder; Eyers, David; Bacon, Jean

    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. This helps those responsible to meet their data management obligations, providing evidence of compliance, and aids in the identification of policy errors and misconfigurations. We present our IFC model and describe and evaluate our IFC architecture and implementation (CamFlow). This comprises an OS level implementation of IFC with support for application management, together with an IFC-enabled middleware. Our contribution is to demonstrate the feasibility of incorporating IFC into cloud services: we show how the incorporation of IFC into underlying IaaS or PaaS provided OSs would address application sharing and protection requirements, and more generally, greatly enhance the trustworthiness of cloud services at all levels, at little overhead, and transparently to tenants. Keywords—Security, Audit, Cloud, Information Flow Control, Middleware, Provenance, Linux Security Module, PaaS, Data Management, Compliance

  • Publication

    Managing Big Data with Information Flow Control

    (2015) Pasquier, Thomas; Singh, Jatinder; Bacon, Jean; Hermant, Olivier

    Concern about data leakage is holding back more widespread adoption of cloud computing by companies and public institutions alike. To address this, cloud tenants/applications are traditionally isolated in virtual machines or containers. But an emerging requirement is for cross-application sharing of data, for example, when cloud services form part of an IoT architecture. Information Flow Control (IFC) is ideally suited to achieving both isolation and data sharing as required. IFC enhances traditional Access Control by providing continuous, data-centric, cross- application, end-to-end control of data flows. However, large-scale data processing is a major requirement of cloud computing and is infeasible under standard IFC. We present a novel, enhanced IFC model that subsumes standard models. Our IFC model supports ‘Big Data’ processing, while retaining the simplicity of standard IFC and enabling more concise, accurate and maintainable expression of policy.