9,475 research outputs found
The case for cloud service trustmarks and assurance-as-a-service
Cloud computing represents a significant economic opportunity for Europe. However, this growth is threatened by adoption barriers largely related to trust. This position paper examines trust and confidence issues in cloud computing and advances a case for addressing them through the implementation of a novel trustmark scheme for cloud service providers. The proposed trustmark would be both active and dynamic featuring multi-modal information about the performance of the underlying cloud service. The trustmarks would be informed by live performance data from the cloud service provider, or ideally an independent third-party accountability and assurance service that would communicate up-to-date information relating to service performance and dependability. By combining assurance measures with a remediation scheme, cloud service providers could both signal dependability to customers and the wider marketplace and provide customers, auditors and regulators with a mechanism for determining accountability in the event of failure or non-compliance. As a result, the trustmarks would convey to consumers of cloud services and other stakeholders that strong assurance and accountability measures are in place for the service in question and thereby address trust and confidence issues in cloud computing
myTrustedCloud: Trusted cloud infrastructure for security-critical computation and data managment
Copyright @ 2012 IEEECloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation
Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. © 2014 Ding et al
Trusted Computing and Secure Virtualization in Cloud Computing
Large-scale deployment and use of cloud computing in industry
is accompanied and in the same time hampered by concerns regarding protection of
data handled by cloud computing providers. One of the consequences of moving
data processing and storage off company premises is that organizations have
less control over their infrastructure. As a result, cloud service (CS) clients
must trust that the CS provider is able to protect their data and
infrastructure from both external and internal attacks. Currently however, such
trust can only rely on organizational processes declared by the CS
provider and can not be remotely verified and validated by an external party.
Enabling the CS client to verify the integrity of the host where the
virtual machine instance will run, as well as to ensure that the virtual
machine image has not been tampered with, are some steps towards building
trust in the CS provider. Having the tools to perform such
verifications prior to the launch of the VM instance allows the CS
clients to decide in runtime whether certain data should be stored- or calculations
should be made on the VM instance offered by the CS provider.
This thesis combines three components -- trusted computing, virtualization technology
and cloud computing platforms -- to address issues of trust and
security in public cloud computing environments. Of the three components,
virtualization technology has had the longest evolution and is a cornerstone
for the realization of cloud computing. Trusted computing is a recent
industry initiative that aims to implement the root of trust in a hardware
component, the trusted platform module. The initiative has been formalized
in a set of specifications and is currently at version 1.2. Cloud computing
platforms pool virtualized computing, storage and network resources in
order to serve a large number of customers customers that use a multi-tenant
multiplexing model to offer on-demand self-service over broad network.
Open source cloud computing platforms are, similar to trusted computing, a
fairly recent technology in active development.
The issue of trust in public cloud environments is addressed
by examining the state of the art within cloud computing security and
subsequently addressing the issues of establishing trust in the launch of a
generic virtual machine in a public cloud environment. As a result, the thesis
proposes a trusted launch protocol that allows CS clients
to verify and ensure the integrity of the VM instance at launch time, as
well as the integrity of the host where the VM instance is launched. The protocol
relies on the use of Trusted Platform Module (TPM) for key generation and data protection.
The TPM also plays an essential part in the integrity attestation of the
VM instance host. Along with a theoretical, platform-agnostic protocol,
the thesis also describes a detailed implementation design of the protocol
using the OpenStack cloud computing platform.
In order the verify the implementability of the proposed protocol, a prototype
implementation has built using a distributed deployment of OpenStack.
While the protocol covers only the trusted launch procedure using generic
virtual machine images, it presents a step aimed to contribute towards
the creation of a secure and trusted public cloud computing environment
S-FaaS: Trustworthy and Accountable Function-as-a-Service using Intel SGX
Function-as-a-Service (FaaS) is a recent and already very popular paradigm in
cloud computing. The function provider need only specify the function to be
run, usually in a high-level language like JavaScript, and the service provider
orchestrates all the necessary infrastructure and software stacks. The function
provider is only billed for the actual computational resources used by the
function invocation. Compared to previous cloud paradigms, FaaS requires
significantly more fine-grained resource measurement mechanisms, e.g. to
measure compute time and memory usage of a single function invocation with
sub-second accuracy. Thanks to the short duration and stateless nature of
functions, and the availability of multiple open-source frameworks, FaaS
enables non-traditional service providers e.g. individuals or data centers with
spare capacity. However, this exacerbates the challenge of ensuring that
resource consumption is measured accurately and reported reliably. It also
raises the issues of ensuring computation is done correctly and minimizing the
amount of information leaked to service providers.
To address these challenges, we introduce S-FaaS, the first architecture and
implementation of FaaS to provide strong security and accountability guarantees
backed by Intel SGX. To match the dynamic event-driven nature of FaaS, our
design introduces a new key distribution enclave and a novel transitive
attestation protocol. A core contribution of S-FaaS is our set of resource
measurement mechanisms that securely measure compute time inside an enclave,
and actual memory allocations. We have integrated S-FaaS into the popular
OpenWhisk FaaS framework. We evaluate the security of our architecture, the
accuracy of our resource measurement mechanisms, and the performance of our
implementation, showing that our resource measurement mechanisms add less than
6.3% latency on standardized benchmarks
To Share or Not to Share in Client-Side Encrypted Clouds
With the advent of cloud computing, a number of cloud providers have arisen
to provide Storage-as-a-Service (SaaS) offerings to both regular consumers and
business organizations. SaaS (different than Software-as-a-Service in this
context) refers to an architectural model in which a cloud provider provides
digital storage on their own infrastructure. Three models exist amongst SaaS
providers for protecting the confidentiality data stored in the cloud: 1) no
encryption (data is stored in plain text), 2) server-side encryption (data is
encrypted once uploaded), and 3) client-side encryption (data is encrypted
prior to upload). This paper seeks to identify weaknesses in the third model,
as it claims to offer 100% user data confidentiality throughout all data
transactions (e.g., upload, download, sharing) through a combination of Network
Traffic Analysis, Source Code Decompilation, and Source Code Disassembly. The
weaknesses we uncovered primarily center around the fact that the cloud
providers we evaluated were each operating in a Certificate Authority capacity
to facilitate data sharing. In this capacity, they assume the role of both
certificate issuer and certificate authorizer as denoted in a Public-Key
Infrastructure (PKI) scheme - which gives them the ability to view user data
contradicting their claims of 100% data confidentiality. We have collated our
analysis and findings in this paper and explore some potential solutions to
address these weaknesses in these sharing methods. The solutions proposed are a
combination of best practices associated with the use of PKI and other
cryptographic primitives generally accepted for protecting the confidentiality
of shared information
FASTCloud: A framework of assessment and selection for trustworthy cloud service based on QoS
By virtue of technology and benefit advantages, cloud computing has
increasingly attracted a large number of potential cloud consumers (PCC) plan
to migrate the traditional business to the cloud service. However, trust has
become one of the most challenging issues that prevent the PCC from adopting
cloud services, especially in trustworthy cloud service selection. Besides, due
to the diversity and dynamic of quality of service (QoS) in the cloud
environment, the existing trust assessment methods based on the single constant
value of QoS attribute and the subjective weight assignment are not good enough
to provide an effective solution for PCCs to identify and select a trustworthy
cloud service among a wide range of functionally-equivalent cloud service
providers (CSPs). To address the challenge, a novel assessment and selection
framework for trustworthy cloud service, FASTCloud, is proposed in this study.
This framework facilitates PCCs to select a trustworthy cloud service based on
their actual QoS requirements. In order to accurately and efficiently assess
the trust level of cloud services, a QoS-based trust assessment model is
proposed. This model represents a trust level assessment method based on the
interval multiple attributes with an objective weight assignment method based
on the deviation maximization to adaptively determine the trust level of
different cloud services provisioned by candidate CSPs. The advantage of the
proposed trust level assessment method in time complexity is demonstrated by
the performance analysis and comparison. The experimental result of a case
study with an open-source dataset shows that the trust model is efficient in
cloud service trust assessment and the FASTCloud can effectively help PCCs
select a trustworthy cloud service
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