ieee based cloud computing abstracts helpful for you to develop project
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This Document Contains IEEE project ideas based on cloud computing technology.With this you may create your own ideas and various application to meet your requirements.TRANSCRIPT
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A Privacy Leakage Upper Bound Constraint-Based Approach forCost-Effective Privacy Preserving of Intermediate Data Sets in Cloud
Abstract
Cloud computing provides massive computation power and storage capacity which enable users
to deploy computation and data-intensive applications without infrastructure investment. Along
the processing of such applications, a large volume of intermediate data sets will be generated,
and often stored to save the cost of recomputing them. However, preserving the privacy of
intermediate data sets becomes a challenging problem because adversaries may recover privacy-
sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in
cloud is widely adopted in existing approaches to address this challenge. But we argue that
encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time
consuming and costly for data-intensive applications to en/decrypt data sets frequently while
performing any operation on them. In this paper, we propose a novel upper bound privacy
leakage constraint-based approach to identify which intermediate data sets need to be encrypted
and which do not, so that privacy-preserving cost can be saved while the privacy requirements of
data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost
of intermediate data sets can be significantly reduced with our approach over existing ones where
all data sets are encrypted.
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CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring
ABSTRACT
Cloud-assisted mobile health (mHealth) monitoring, which applies the prevailing mobile
communications and cloud computing technologies to provide feedback decision support, has
been considered as a revolutionary approach to improving the quality of healthcare service while
lowering the healthcare cost. Unfortunately, it also poses a serious risk on both clients’ privacy
and intellectual property of monitoring service providers, which could deter the wide adoption of
mHealth technology. This paper is to address this important problem and design a cloud-assisted
privacy preserving mobile health monitoring system to protect the privacy of the involved parties
and their data. Moreover, the outsourcing decryption technique and a newly proposed key private
proxy reencryption are adapted to shift the computational complexity of the involved parties to
the cloud without compromising clients’ privacy and service providers’ intellectual property.
Finally, our security and performance analysis demonstrates the effectiveness of our proposed
design.
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Enabling Dynamic Data and Indirect Mutual Trust for Cloud Computing Storage Systems
AbstractStorage-as-a-Service offered by cloud service providers (CSPs) is a paid facility
that enables organizations to outsource their sensitive data to be stored on remote
servers. In this paper, we propose a cloud-based storage scheme that allows the
data owner to benefit from the facilities offered by the CSP and enables indirect
mutual trust between them. The proposed scheme has four important features: (i) it
allows the owner to outsource sensitive data to a CSP, and perform full block-level
dynamic operations on the outsourced data, i.e., block modification, insertion,
deletion, and append, (ii) it ensures that authorized users (i.e., those who have
the right to access the owner’s file) receive the latest version of the outsourced
data, (iii) it enables indirect mutual trust between the owner and the CSP, and (iv)
it allows the owner to grant or revoke access to the outsourced data. We discuss the
security issues of the proposed scheme. Besides, we justify its performance
through theoretical analysis and a prototype implementation on Amazon cloud
platform to evaluate storage, communication, and computation overheads.
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On Data Staging Algorithms for Shared Data Accesses in Clouds
AbstractIn this paper, we study the strategies for efficiently achieving data staging and
caching on a set of vantage sites in a cloud system with a minimum cost. Unlike
the traditional research, we do not intend to identify the access patterns to facilitate
the future requests. Instead, with such a kind of information presumably known in
advance, our goal is to efficiently stage the shared data items to predetermined
sites at advocated time instants to align with the patterns while minimizing the
monetary costs for caching and transmitting the requested data items. To this end,
we follow the cost and network models in [1] and extend the analysis to multiple
data items, each with single or multiple copies. Our results show that under
homogeneous cost model, when the ratio of transmission cost and caching cost is
low, a single copy of each data item can efficiently serve all the user requests.
While in multicopy situation, we also consider the tradeoff between the
transmission cost and caching cost by controlling the upper bounds of
transmissions and copies. The upper bound can be given either on per-item basis or
on all-item basis. We present efficient optimal solutions based on dynamic
programming techniques to all these cases provided that the upper bound is
polynomially bounded by the number of service requests and the number of
distinct data items. In addition to the homogeneous cost model, we also briefly
discuss this problem under a heterogeneous cost model with some simple yet
practical restrictions and present a 2-approximation algorithm to the general case.
We validate our findings by implementing a data staging solver, whereby
conducting extensive simulation studies on the behaviors of the algorithms.
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QoS Ranking Prediction for Cloud Services
Abstract
Cloud computing is becoming popular. Building high-quality cloud applications is
a critical research problem. QoS rankings provide valuable information for making
optimal cloud service selection from a set of functionally equivalent service
candidates. To obtain QoS values, real-world invocations on the service candidates
are usually required. To avoid the time-consuming and expensive real-world
service invocations, this paper proposes a QoS ranking prediction framework for
cloud services by taking advantage of the past service usage experiences of other
consumers. Our proposed framework requires no additional invocations of cloud
services when making QoS ranking prediction. Two personalized QoS ranking
prediction approaches are proposed to predict the QoS rankings directly.
Comprehensive experiments are conducted employing real-world QoS data,
including 300 distributed users and 500 realworld web services all over the world.
The experimental results show that our approaches outperform other competing
approaches.