Person:

Pasquier, Thomas

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.

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.

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.

Security concerns are widely seen as an obstacle to the adoption of cloud computing solutions. Information Flow Control (IFC) is a well understood Mandatory Access Control methodology. The earliest IFC models targeted security in a centralised environment, but decentralised forms of IFC have been designed and implemented, often within academic research projects. As a result, there is potential for decentralised IFC to achieve better cloud security than is available today. In this paper we describe the properties of cloud computing— Platform-as-a-Service clouds in particular—and review a range of IFC models and implementations to identify opportunities for using IFC within a cloud computing context. Since IFC security is linked to the data that it protects, both tenants and providers of cloud services can agree on security policy, in a manner that does not require them to understand and rely on the particulars of the cloud software stack in order to effect enforcement. Index Terms—Cloud, data security, information flow, informa- tion flow control (IFC).

Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system’s behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.

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

With the rapid increase in uptake of cloud services, issues of data management are becoming increasingly prominent. There is a clear, outstanding need for the ability for specified policy to control and track data as it flows throughout cloud infrastructure, to ensure that those responsible for data are meeting their obligations. This paper introduces Information Flow Audit, an approach for tracking information flows within cloud infrastructure. This builds upon CamFlow (Cambridge Flow Control Architecture), a prototype implementation of our model for data-centric security in PaaS clouds. CamFlow enforces Information Flow Control policy both intra-machine at the kernel-level, and inter-machine, on message exchange. Here we demonstrate how CamFlow can be extended to provide data-centric audit logs akin to provenance metadata in a format in which analyses can easily be automated through the use of standard graph processing tools. This allows detailed understanding of the overall system. Combining a con- tinuously enforced data-centric security mechanism with mean- ingful audit empowers tenants and providers to both meet and demonstrate compliance with their data management obligations.