Distributed Middleware Enforcement of Event Flow Security Policy

Migliavacca, Matteo; Papagiannis, Ioannis; Eyers, David; Shand, Brian; Bacon, Jean; Pietzuch, Peter

doi:10.1007/978-3-642-16955-7_17

Cited by 8 publications

(9 citation statements)

References 9 publications

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“…The work developing a Data Distribution Infrastructure (DDI) [15] works to ensure distributed policy enforcement by integrating services into local enforcement infrastructures but simply assumes remote trust capabilities are present. Various other contributions to the area rely on homogeneous components [25], expect policy breaches to be prosecuted via review audits [7], work under assumptions that leave trust establishment as out of scope [13] [20] [19], or simply acknowledge that trusted attestation services could strengthen the system without considering how to incorporate them [6]. Our work looks to facilitate verifiable attribute collection, which allows informed policy decisions without strict component knowledge prior to policy authoring.…”

Section: B Related Workmentioning

confidence: 99%

Distributed Enforcement of Sticky Policies with Flexible Trust

Brown

Blough

2015

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium

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In this paper, we describe an approach to distributed enforcement of sticky policies in heterogeneous hardware and software environments. These heterogeneous environments might have differing mechanisms for attesting to their security capabilities and data sources might specify different levels of trust for different data items. Such an environment requires highly flexible policy specification and enforcement mechanisms. We employ sticky policies that travel with data wherever it travels, and we separate them into two components, a hosting policy and a usage policy. Hosting policies are used to ensure that data are transferred only to entities that are provably capable of providing local enforcement and only further transferring data under the same policies. Usage policies confer access, viewing, and update capabilities on users based on their attributes. The approach is supported by attribute-based certificates and policies, which include what authorities are trusted to certify attributes. In addition to presenting a full description of the approach, we report on a prototype implementation that includes all of the aforementioned components and also makes use of a modified version of Excel we developed to track security labels as data move through spreadsheets that are being shared by multiple users across different systems. II. INTRODUCTIONIn the information age in which we live, organizations maintain large amounts of data, much of it sensitive. In many situations, there could be great value in sharing these data with individuals or partners outside of an organization's controlled hardware/software infrastructure. For example, in the domain of medicine, health professionals would like to safely access patient information on their laptops or even mobile devices, and controlled sharing of such data for both health care and research purposes has obvious scientific and social benefits. Clearly, inadvertent disclosure of health information could have severe consequences for patients with sensitive conditions. Thus, although the benefits of information sharing are great, the risks of data breaches if the information is not properly handled after it is shared are often greater. A similar analysis can be made concerning sensitive customer information held by commercial organizations, information maintained in databases by law enforcement organizations, etc.Organizations that allow sensitive data to be disseminated need assurances that the systems receiving the data will control access so that users only see the information appropriate to their level of authorization, maintain sensitivity labels on information as data are propagated within the system, and only further disseminate the data to other systems if those systems meet the requirements of the data owner. Enabling concepts for this vision are sticky policies, wherein policies on data usage are propagated along with data as they move within and between systems, and information flow control (IFC), where sensitive information is tagged, tags propagate with data as t...

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Section: B Related Workmentioning

confidence: 99%

Distributed Enforcement of Sticky Policies with Flexible Trust

Brown

Blough

2015

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium

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“…This has made it difficult to operate for the common user. DEFCON 31 and SafeWeb 32 both track data at message level. DStar 29 facilitates IFC in distributed systems.…”

Section: Related Workmentioning

confidence: 99%

Secure‐CamFlow: A device‐oriented security model to assist information flow control systems in cloud environments for IoTs

Khurshid

Khan

et al. 2018

Concurrency and Computation

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Recent developments in the cloud technologies have motivated the migration of distributed large systems, specifically the Internet of Things to the cloud architecture. Since Internet of Things consist of a vast network and variety of objects, the cloud platform proves to be an ideal option. It is essential for the proper functioning of the Internet of Things to be able to share data among the system processes. The biggest problem faced during the transition of the IoTs to the cloud is the security of data especially while data sharing within the cloud and among its tenants. Information Flow Control mechanisms are one of the many solutions to enable a controlled sharing of data.Integration of Information Flow Control Systems to the existing architecture requires various levels of re-engineering efforts. Moreover, most of the Information Flow Control systems focus on data flow within the cloud and neglect the security and integrity of data while it is being transferred to the cloud from various devices. This research focuses on securing the entire process of data migration to cloud from devices while the in-cloud data flow is monitored by the Information Flow Control policies specified by the users. We have developed a prototype for the proposed model, and results are evaluated on the basis of energy consumption and execution time. As proposed model provides security services such as privacy, integrity, and authentication, hence it takes more execution time and consumes more energy as compared with the existing model. KEYWORDS cloud computing, information flow control, IoT, privacy, security 1 INTRODUCTION Internet of Things (IoTs) is the emerging technology that connects numerous smart devices to form a larger system. The interconnected components communicate continuously like a single entity. The resulting systems of IoTs are the smart cities, smart homes, smart grids, intelligent health, transportation facilities, and smart metering. 1 The smart devices in IoTs are equipped with sensors and actuators and provide remote controlled access. The IoTs is based on a widespread network of devices all over the world. 2 For IoTs to function, the system requires intercommunication of processes, storage, and sharing of information. The aforesaid objectives can be achieved by connecting all IoTs devices to a single platform such as cloud. 3 Currently, a major area of research is on the integration of IoTs with cloud computing. The integration of IoTs with the cloud provides a solution to most of the issues of the existing IoTs architecture such as (a) the application can be deployed in a matter of minutes, (b) the device becomes independent of location, (c) information can be shared with a variety of audience who has permission to access the resource anytime and anywhere, and (d) data sharing among the tenants is easy. 4,5The integration of IoTs with cloud raises some other concerns. 4 The existing issues are providing secure communications, identification, and manipulation of sensitive data, provider-based security against...

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“…Any solution that detects data leakage should be easily deployable in today's PaaS environments. Existing information flow tracking (IFT) systems [83] can track the propagation of data through a system but require substantial changes to the OS or cloud stack, or make specific assumptions about the architecture of applications [52,98,58]. Approaches for remotely verifying the software installed on cloud platforms based on trusted hardware [80] require costly hardware and software upgrades in a cloud environment.…”

Section: Requirements and Existing Solutionsmentioning

confidence: 99%

“…Information flow control (IFC) techniques have been employed in a variety of contexts, including at the OS [97,52], middleware [58] and language levels [93,67], databases [82] and in distributed settings [98].…”

Section: Related Workmentioning

confidence: 99%

CloudSafetyNet

Priebe

Muthukumaran

O'Keeffe

et al. 2014

Proceedings of the 6th Edition of the ACM Workshop on Cloud Computing Security

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When tenants deploy applications under the control of third-party cloud providers, they must trust the provider's security mechanisms for inter-tenant isolation, resource sharing and access control. Despite a provider's best efforts, accidental data leakage may occur due to misconfigurations or bugs in the cloud platform. Especially in Platform-as-a-Service (PaaS) clouds, which rely on weaker forms of isolation, the potential for unnoticed data leakage is high. Prior work to raise tenants' trust in clouds relies on attestation, which limits the management flexibility of providers, or fine-grained data tracking, which has high overheads.We describe CloudSafetyNet (CSN), a lightweight monitoring framework that gives tenants visibility into the propagation of their application data in a cloud environment with low performance overhead. It exploits the incentive of tenants to co-operate with each other to detect accidental data leakage. CSN transparently adds opaque security tags to a subset of form fields in HTTP requests, using a client-side JavaScript library. Socket-level monitors maintain a log of observed tags flowing between application components. Tenants retrieve their logs and identify foreign tags that indicate data leakage. To check the correct operation of CSN, tenants send probe requests with known tags and verify that monitors are logging correctly. Using an implementation of CSN deployed on the OpenShift and AppScale PaaS platforms, we show that it can discover misconfigurations and bugs with a negligible performance impact.

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Distributed Middleware Enforcement of Event Flow Security Policy

Cited by 8 publications

References 9 publications

Distributed Enforcement of Sticky Policies with Flexible Trust

Distributed Enforcement of Sticky Policies with Flexible Trust

Secure‐CamFlow: A device‐oriented security model to assist information flow control systems in cloud environments for IoTs

CloudSafetyNet

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