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1. SEABIRDS SOLUTION
SEABIRDS SOLUTION
IEEE 2014 – 2015
SOFTWARE PROJECTS IN VARIOUS DOMAINS
|JAVA|J2ME |J2EE|
|DOTNET|MATLAB|NS2|
SBGC, 24/83, O Block,
Sathish Kumar – 09003012150
MMDA COLONY
ARUMBAKKAM
CHENNAI-600106
SBGC 4th FLOOR SURYA
COMPLEX,
SINGARATHOPE BUS STOP,
OLD MADURAI ROAD,
TRICHY-620002
Web: www.ieeeproject.in
E-Mail: ieeeproject@hotmail.com
Trichy
Mobile:- 09003012150
Phone:- 0431-4013174
Chennai
Mobile: - 09944361169

2. SEABIRDS SOLUTION
TECHNOLOGY: JAVA
DOMAIN: CLOUD COMPUTING
S. No. IEEE TITLE ABSTRACT IEEE YEAR
1. Toward Privacy
Preserving and
Sathish Kumar – 09003012150
Collusion
Resistance in
a Location Proof
Updating System
By leveraging virtual machine (VM)
technology which provides performance and
fault isolation, cloud resources can be
provisioned on demand in a fine grained,
multiplexed manner rather than in monolithic
pieces. By integrating volunteer computing
into cloud architectures, we envision a
gigantic self-organizing cloud (SOC) being
formed to reap the huge potential of untapped
commodity computing power over the
Internet. Toward this new architecture where
each participant may autonomously act as
both resource consumer and provider, we
propose a fully distributed, VM-multiplexing
resource allocation scheme to manage
decentralized resources. Our approach not
only achieves maximized resource utilization
using the proportional share model (PSM),
but also delivers provably and adaptively
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3. SEABIRDS SOLUTION
optimal execution efficiency. We also design
a novel multi attribute range query protocol
for locating qualified nodes. Contrary to
existing solutions which often generate bulky
messages per request, our protocol produces
only one lightweight query message per task
on the Content Addressable Network (CAN).
It works effectively to find for each task its
qualified resources under a randomized policy
that mitigates the contention among
requesters. We show the SOC with our
optimized algorithms can make an
improvement by 15-60 percent in system
throughput than a P2P Grid model. Our
solution also exhibits fairly high adaptability
in a dynamic node-churning environment.
2. Scalable and
Secure Sharing of
Personal Health
Records in Cloud
Computing Using
Sathish Kumar – 09003012150
Attribute-Based
Encryption
Personal health record (PHR) is an emerging
patient-centric model of health information
exchange, which is often outsourced to be
stored at a third party, such as cloud
providers. However, there have been wide
privacy concerns as personal health
information could be exposed to those third
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4. SEABIRDS SOLUTION
party servers and to Un authorized parties. To
assure the patients' control over access to their
own PHRs, it is a promising method to
encrypt the PHRs before outsourcing. Yet,
issues such as risks of privacy exposure,
scalability in key management, flexible
access, and efficient user revocation, have
remained the most important challenges
toward achieving fine-grained,
cryptographically enforced data access
control. In this paper, we propose a novel
patient-centric framework and a suite of
mechanisms for data access control to PHRs
stored in semitrusted servers. To achieve fine-grained
and scalable data access control for
PHRs, we leverage attribute-based encryption
(ABE) techniques to encrypt each patient's
PHR file. Different from previous works in
secure data outsourcing, we focus on the
multiple data owner scenario, and divide the
users in the PHR system into multiple security
domains that greatly reduces the key
management complexity for owners and
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5. SEABIRDS SOLUTION
users. A high degree of patient privacy is
guaranteed simultaneously by exploiting
multiauthority ABE. Our scheme also enables
dynamic modification of access policies or
file attributes, supports efficient on-demand
user/attribute revocation and break-glass
access under emergency scenarios. Extensive
analytical and experimental results are
presented which show the security,
scalability, and efficiency of our proposed
scheme.
3. On Data Staging
Algorithms for
Shared Data
Accesses in
Sathish Kumar – 09003012150
Clouds
In 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
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6. SEABIRDS SOLUTION
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
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7. SEABIRDS SOLUTION
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.
Sathish Kumar – 09003012150
4. Decentralized
Access Control
with Anonymous
Authentication of
Data Stored in
Clouds
We propose a new decentralized
access control scheme for secure data storage
in clouds that supports anonymous
authentication. In the proposed scheme, the
cloud verifies the authenticity of the series
without knowing the user’s identity before
storing data. Our scheme also has the added
feature of access control in which only valid
users are able to decrypt the stored
information. The scheme prevents replay
attacks and supports creation, modification,
and reading data stored in the cloud. We also
address user revocation. Moreover, our
authentication and access control scheme is
decentralized and robust, unlike other access
control schemes designed for clouds which
are centralized. The communication,
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8. SEABIRDS SOLUTION
computation, and storage overheads are
comparable to centralized approaches.
5. Modeling of
Distributed File
Systems for
Sathish Kumar – 09003012150
Practical
Performance
Analysis
Abstract—Cloud computing has received
significant attention recently. Delivering
quality guaranteed services in clouds is highly
desired. Distributed file systems (DFSs) are
the key component of any cloud-scale data
processing middleware. Evaluating the
performance of DFSs is accordingly very
important. To avoid cost for late life cycle
performance fixes and architectural redesign,
providing performance analysis before the
deployment of DFSs is also particularly
important. In this paper, we propose a
systematic and practical performance analysis
framework, driven by architecture and design
models for defining the structure and behavior
of typical master/slave DFSs. We put forward
a configuration guideline for specifications of
configuration alternatives of such DFSs, and a
practical approach for both qualitatively and
quantitatively performance analysis of DFSs
with various configuration settings in a
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9. SEABIRDS SOLUTION
systematic way. What distinguish our
approach from others is that 1) most of
existing works rely on performance
measurements under a variety of
workloads/strategies, comparing with other
DFSs or running application programs, but
our approach is based on architecture and
design level models and systematically
derived performance models; 2) our approach
is able to both qualitatively and quantitatively
evaluate the performance of DFSs; and 3) our
approach not only can evaluate the overall
performance of a DFS but also its components
and individual steps. We demonstrate the
effectiveness of our approach by evaluating
Hadoop distributed file system (HDFS). A
series of real-world experiments on EC2
(Amazon Elastic Compute Cloud), Tansuo
and Inspur Clusters, were conducted to
qualitatively evaluate the effectiveness of our
approach. We also performed a set of
experiments of HDFS on EC2 to
quantitatively analyze the performance and
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10. SEABIRDS SOLUTION
limitation of the metadata server of DFSs.
Results show that our approach can achieve
sufficient performance analysis. Similarly, the
proposed approach could be also applied to
evaluate other DFSs such as MooseFS, GFS,
and zFS.
Sathish Kumar – 09003012150
6. Balancing
Performance,
Accuracy, and
Precision for
Secure Cloud
Transactions
Abstract—In distributed transactional
database systems deployed over cloud
servers, entities cooperate to form proofs of
authorizations that are justified by collections
of certified credentials. These proofs and
credentials may be evaluated and collected
over extended time periods under the risk of
having the underlying authorization policies
or the user credentials being in inconsistent
states. It therefore becomes possible for
policy-based authorization systems to make
unsafe decisions that might threaten sensitive
resources. In this paper, we highlight the
criticality of the problem. We then define the
notion of trusted transactions when dealing
with proofs of authorization. Accordingly, we
propose several increasingly stringent levels
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11. SEABIRDS SOLUTION
of policy consistency constraints, and present
different enforcement approaches to
guarantee the trustworthiness of transactions
executing on cloud servers. We propose a
Two-Phase Validation Commit protocol as a
solution, which is a modified version of the
basic Two-Phase Validation Commit
protocols. We finally analyze the different
approaches presented using both analytical
evaluation of the overheads and simulations
to guide the decision makers to which
approach to use.
7. A Scalable Two-
Phase Top-Down
Sathish Kumar – 09003012150
Specialization
Approach for
Data
Anonymization
Using
MapReduce on
Cloud
Abstract—A large number of cloud services
require users to share private data like
electronic health records for data analysis or
mining, bringing privacy concerns.
Anonymizing data sets via generalization to
satisfy certain privacy requirements such as
kanonymity is a widely used category of
privacy preserving techniques. At present, the
scale of data in many cloud applications
increases tremendously in accordance with
the Big Data trend, thereby making it a
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12. SEABIRDS SOLUTION
challenge for commonly used software tools
to capture, manage, and process such large-scale
data within a tolerable elapsed time. As
a result, it is a challenge for existing
anonymization approaches to achieve privacy
preservation on privacy-sensitive large-scale
data sets due to their insufficiency of
scalability. In this paper, we propose a
scalable two-phase top-down specialization
(TDS) approach to anonymize large-scale
data sets using the MapReduce framework on
cloud. In both phases of our approach, we
deliberately design a group of innovative
MapReduce jobs to concretely accomplish the
specialization computation in a highly
scalable way. Experimental evaluation results
demonstrate that with our approach, the
scalability and efficiency of TDS can be
significantly improved over existing
approaches .
Sathish Kumar – 09003012150