Person:

Pasquier, Thomas

In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientfic fields exhibit distressingly low rates of repeatability and reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and effciency of reporting, which contributes to issues of reproducibility. Data provenance aids both repeatability and reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers.

There is a clear, outstanding need for new security mechanisms that allow data to be managed and controlled within the cloud-enabled Internet of Things. Towards this, we propose an approach based on Information Flow Control (IFC) that allows: (1) the continuous, end-to-end enforcement of data flow policy, and (2) the generation of provenance-like audit logs to demon- strate policy adherence and contractual/regulatory compliance. Further, we discuss the role of Trusted Platform Modules (TPMs) in supporting such a system, by providing hardware roots of trust. TPMs can be leveraged to validate software configurations, including the IFC enforcement mechanism, both in the cloud and externally via remote attestation.

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.

We present FRAPpuccino (or FRAP), a provenance- based fault detection mechanism for Platform as a Ser- vice (PaaS) users, who run many instances of an appli- cation on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance’s execution to that of the model. Any in- stance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and ex- perimental results showing that it can accurately detect application anomalies.

Unikernels are a rapidly emerging technology in the world of cloud computing. Unikernels build on research on library operating systems to deliver smaller, faster and more secure virtual machines, specifically optimised for a single application service. These features are especially useful in cost or resource constrained environments. However, as with any new technology, early adopters need to master many technical details, and understand many aspects of the mechanisms used to build and deploy unikernels. Both of these factors may slow adoption rates. In this paper, we present our initial experiments into the use of an approach for building unikernels that is accessible to those whose technical expertise is focused on web development. We present PHP2Uni: a tool chain that takes a website built from PHP files—PHP remains the most widely used web language— and builds a resource-efficient unikernel image from them, while requiring little knowledge of the underlying operating system software complexity.

Internet of Things (IoT) applications, systems and services are subject to law. We argue that for the IoT to develop lawfully, there must be technical mechanisms that allow the enforcement of specified policy, such that systems align with legal realities. The audit of policy enforcement must assist the apportionment of liability, demonstrate compliance with regulation, and indicate whether policy correctly captures le- gal responsibilities. As both systems and obligations evolve dynamically, this cycle must be continuously maintained. This poses a huge challenge given the global scale of the IoT vision. The IoT entails dynamically creating new ser- vices through managed and flexible data exchange. Data management is complex in this dynamic environment, given the need to both control and share information, often across federated domains of administration. We see middleware playing a key role in managing the IoT. Our vision is for a middleware-enforced, unified policy model that applies end-to-end, throughout the IoT. This is because policy cannot be bound to things, applications, or administrative domains, since functionality is the result of composition, with dynamically formed chains of data flows. We have investigated the use of Information Flow Control (IFC) to manage and audit data flows in cloud computing; a domain where trust can be well-founded, regulations are more mature and associated responsibilities clearer. We feel that IFC has great potential in the broader IoT context. However, the sheer scale and the dynamic, federated nature of the IoT pose a number of significant research challenges.

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

Abstract—The adoption of cloud computing is increasing and its use is becoming widespread in many sectors. As the proportion of services provided using cloud computing increases, legal and regulatory issues are becoming more significant. In this paper we explore how an Information Flow Audit (IFA) mechanism, that provides key data regarding provenance, can be used to verify compliance with regulatory and contractual duty, and survey potential extensions. We explore the use of IFA for such a purpose through a smart electricity metering use case derived from a French Data Protection Agency recommendation.

The adoption of cloud computing is increasing and its use is becoming widespread in many sectors. As cloud service provision increases, legal and regulatory issues become more significant. In particular, the international nature of cloud provision raises concerns over the location of data and the laws to which they are subject. In this paper we investigate Information Flow Control (IFC) as a possible technical solution to expressing, enforcing and demonstrating compliance of cloud computing systems with policy requirements inspired by data protection and other laws. We focus on geographic location of data, since this is the paradigmatic concern of legal/regulatory requirements on cloud computing and, to date, has not been met with robust technical solutions and verifiable data flow audit trails.

The need to share data across applications is be- coming increasingly evident. Current cloud isolation mechanisms focus solely on protection, such as containers that isolate at the OS-level, and virtual machines that isolate through the hypervi- sor. However, by focusing rigidly on protection, these approaches do not provide for controlled sharing. This paper presents how Information Flow Control (IFC) offers a flexible alternative. As a data-centric mechanism it enables strong isolation when required, while providing continuous, fine grained control of the data being shared. An IFC-enabled cloud platform would ensure that policies are enforced as data flows across all applications, without requiring any special sharing mechanisms.