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IEEE 2015-2016 DOTNET PROJECTS
S3001 - DETECTION AND RECTIFICATION OF DISTORTED FINGERPRINTS
Elastic distortion of fingerprints is one of the major causes for false non-match. While
this problem affects all fingerprint recognition applications, it is especially dangerous in negative
recognition applications, such as watchlist and deduplication applications. In such applications,
malicious users may purposely distort their fingerprints to evade identification. In this paper, we
proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint
image. Distortion detection is viewed as a two-class classification problem, for which the
registered ridge orientation map and period map of a fingerprint are used as the feature vector
and a SVM classifier is trained to perform the classification task. Distortion rectification (or
equivalently distortion field estimation) is viewed as a regression problem, where the input is a
distorted fingerprint and the output is the distortion field. To solve this problem, a database
(called reference database) of various distorted reference fingerprints and corresponding
distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of
the input fingerprint is found in the reference database and the corresponding distortion field is
used to transform the input fingerprint into a normal one. Promising results have been obtained
on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua
Distorted Fingerprint database, and the NIST SD27 latent fingerprint database.
S3002 - PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA
WITH GROUP USER REVOCATION
The advent of the cloud computing makes storage outsourcing becomes a rising trend,
which promotes the secure remote data auditing a hot topic that appeared in the research
literature. Recently some research considers the problem of secure and efficient public data
integrity auditing for shared dynamic data. However, these schemes are still not secure against
the collusion of cloud storage server and revoked group users during user revocation in practical
cloud storage system. In this paper, we figure out the collusion attack in the exiting scheme and
provide an efficient public integrity auditing scheme with secure group user revocation based on
vector commitment and verifier-local revocation group signature. We design a concrete scheme
based on our scheme definition. Our scheme supports the public checking and efficient user
revocation and also some nice properties, such as confidently, efficiency, countability and
traceability of secure group user revocation. Finally, the security and experimental analysis show
that compared with its relevant schemes our scheme is also secure and efficient.
S3003 - KEY-AGGREGATE SEARCHABLE ENCRYPTION (KASE) FOR GROUP
DATA SHARING VIA CLOUD STORAGE

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The capability of selectively sharing encrypted data with different users via public cloud
storage may greatly ease security concerns over inadvertent data leaks in the cloud. A key
challenge to designing such encryption schemes lies in the efficient management of encryption
keys. The desired flexibility of sharing any group of selected documents with any group of users
demands different encryption keys to be used for different documents. However, this also
implies the necessity of securely distributing to users a large number of keys for both encryption
and search, and those users will have to securely store the received keys, and submit an equally
large number of keyword trapdoors to the cloud in order to perform search over the shared data.
The implied need for secure communication, storage, and complexity clearly renders the
approach impractical. In this paper, we address this practical problem, which is largely neglected
in the literature, by proposing the novel concept of key aggregate searchable encryption (KASE)
and instantiating the concept through a concrete KASE scheme, in which a data owner only
needs to distribute a single key to a user for sharing a large number of documents, and the user
only needs to submit a single trapdoor to the cloud for querying the shared documents. The
security analysis and performance evaluation both confirm that our proposed schemes are
provably secure and practically efficient.
S3004 - A DYNAMIC SECURE GROUP SHARING FRAMEWORK IN PUBLIC CLOUD
COMPUTING
With the popularity of group data sharing in public cloud computing, the privacy and
security of group sharing data have become two major issues. The cloud provider cannot be
treated as a trusted third party because of its semi-trust nature, and thus the traditional security
models cannot be straightforwardly generalized into cloud based group sharing frameworks. In
this paper, we propose a novel secure group sharing framework for public cloud, which can
effectively take advantage of the cloud servers' help but have no sensitive data being exposed to
attackers and the cloud provider. The framework combines proxy signature, enhanced TGDH
and proxy re-encryption together into a protocol. By applying the proxy signature technique, the
group leader can effectively grant the privilege of group management to one or more chosen
group members. The enhanced TGDH scheme enables the group to negotiate and update the
group key pairs with the help of cloud servers, which does not require all of the group members
been online all the time. By adopting proxy re-encryption, most computationally intensive
operations can be delegated to cloud servers without disclosing any private information.
Extensive security and performance analysis shows that our proposed scheme is highly efficient
and satisfies the security requirements for public cloud based secure group sharing.
S3005 - JOINT CONGESTION CONTROL AND ROUTING OPTIMIZATION: AN
EFFICIENT SECOND-ORDER DISTRIBUTED APPROACH

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Distributed joint congestion control and routing optimization has received a significant
amount of attention recently. To date, however, most of the existing schemes follow a key idea
called the back-pressure algorithm. Despite having many salient features, the first-order sub
gradient nature of the back-pressure based schemes results in slow convergence and poor delay
performance. To overcome these limitations, in this paper, we make a first attempt at developing
a second-order joint congestion control and routing optimization framework that offers utility-
optimality, queue-stability, fast convergence, and low delay. Our contributions in this paper are
three-fold: i) we propose a new second-order joint congestion control and routing framework
based on a primal-dual interior-point approach; ii)we establish utility-optimality and queue-
stability of the proposed second-order method; and iii) we show how to implement the proposed
second-order method in a distributed fashion.
S3006 - FUZZY BASED ENERGY EFFICIENT MULTICAST ROUTING FOR AD-HOC
NETWORK
Ad-hoc network is an infrastructure less wireless network. Since ad-hoc networks are
self-organizing, rapidly deployable wireless networks, they are highly suitable for various
applications. Every nodes of ad-hoc network are connected dynamically in an arbitrary manner.
There is no default router available in this network because all nodes of this network behave as
routers and take part in discovery and maintenance of routes to other nodes. Ad-hoc nodes are
powered by batteries with limited capacity due to its distributed nature. Therefore energy
consumption occurs due to sending a packet, receiving a packet and to select next hop node.
Hence, the present paper proposes a routing protocol, named Fuzzy Based Energy Efficient
Multicast Routing (FBEEMR) for ad-hoc network. The basic idea of FBEEMR is to select the
best path which reduces energy consumption of ad-hoc nodes based on fuzzy logic. This protocol
is mainly used to extend the lifetime of ad-hoc network with respect to energy efficient multicast
routing by calculating route lifetime values for each route. Based on the comprehensive
simulations and comparative study of same with other existing protocols, it is observed that
proposed routing protocol contributes to the performance improvements in terms of energy
efficiency.
S3007 - ENABLING CLOUD STORAGE AUDITING WITH KEY-EXPOSURE
RESISTANCE
Cloud storage auditing is viewed as an important service to verify the integrity of the data
in public cloud. Current auditing protocols are all based on the assumption that the client's secret
key for auditing is absolutely secure. However, such assumption may not always be held, due to
the possibly weak sense of security and/or low security settings at the client. If such a secret key
for auditing is exposed, most of the current auditing protocols would inevitably become unable
to work. In this paper, we focus on this new aspect of cloud storage auditing. We investigate how

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to reduce the damage of the client's key exposure in cloud storage auditing, and give the first
practical solution for this new problem setting. We formalize the definition and the security
model of auditing protocol with key exposure resilience and propose such a protocol. In our
design, we employ the binary tree structure and the preorder traversal technique to update the
secret keys for the client. We also develop a novel authenticator construction to support the
forward security and the property of blockless verifiability. The security proof and the
performance analysis show that our proposed protocol is secure and efficient.
S3008 - A COMPUTATIONAL DYNAMIC TRUST MODEL FOR USER
AUTHORIZATION
Development of authorization mechanisms for secure information access by a large
community of users in an open environment is an important problem in the ever-growing Internet
world. In this paper we propose a computational dynamic trust model for user authorization,
rooted in findings from social science. Unlike most existing computational trust models, this
model distinguishes trusting belief in integrity from that in competence in different contexts and
accounts for subjectivity in the evaluation of a particular trustee by different trusters. Simulation
studies were conducted to compare the performance of the proposed integrity belief model with
other trust models from the literature for different user behavior patterns. Experiments show that
the proposed model achieves higher performance than other models especially in predicting the
behavior of unstable users.
S3009 - CLOUDARMOR: SUPPORTING REPUTATION BASED TRUST
MANAGEMENT FOR CLOUD SERVICES
Trust management is one of the most challenging issues for the adoption and growth of
cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services
introduces several challenging issues such as privacy, security, and availability. Preserving
consumers’ privacy is not an easy task due to the sensitive information involved in the
interactions between consumers and the trust management service. Protecting cloud services
against their malicious users (e.g., such users might give misleading feedback to disadvantage a
particular cloud service) is a difficult problem. Guaranteeing the availability of the trust
management service is another significant challenge because of the dynamic nature of cloud
environments. In this article, we describe the design and implementation of CloudArmor, a
reputation-based trust management framework that provides a set of functionalities to deliver
Trust as a Service (TaaS), which includes i) a novel protocol to prove the credibility of trust
feedbacks and preserve users’ privacy, ii) an adaptive and robust credibility model for measuring
the credibility of trust feedbacks to protect cloud services from malicious users and to compare
the trustworthiness of cloud services, and iii) an availability model to manage the availability of
the decentralized implementation of the trust management service. The feasibility and benefits of

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our approach have been validated by a prototype and experimental studies using a collection of
real-world trust feedbacks on cloud services.
S30010 - SERVING THE READERS OF SCHOLARLY DOCUMENTS: A GRAND
CHALLENGE FOR THE INTROSPECTIVE DIGITAL LIBRARY
The scholarly literature produced by human civilization will soon be considered small
data, able to be portably conveyed by the network and carried on personal machines. This semi-
structured text centric knowledge base is a focus of attention for scholars, as the wealth of facts,
facets and connections in scholarly documents are large. Such machine analysis can derive
insights that can inform policy makers, academic and industrial management, as well as scholars
as authors themselves. There is another underserved community of scholarly document users that
has been overlooked: the readers themselves. I call for the community to put more efforts
towards supporting our own scholars (especially beginning scholars, new to the research process)
with automation from information retrieval and natural language processing. Techniques that
mine information from within the full text of a document could be used to introspect a digital
library's materials, inferring better search metadata, improving scholarly document
recommendation, and aiding the understanding of the text, figures, presentations and citations of
our scholarly literature. Such an introspective digital library will enable scholars to assemble an
understanding of other scholars' work more efficiently, and provide downstream machine
reading applications with input for their analytics.
S30011 - IMAGE INPAINTING BY MINIMUM ENERGY RESTORATION WITH
EDGE-PRIORITIZED FILLING ORDER
We propose a texture synthesis image inpainting method that minimizes energy function
with the filling order which facilitates propagation of edge from the data region to the target
region. As a result, the proposed method provides plausible restoration while propagating
information of edge for the target region. Experimental results show the validity of the proposed
method.
S30012 - ON LOCAL PREDICTION BASED REVERSIBLE WATERMARKING
The use of local prediction in difference expansion reversible watermarking provides
very good results, but at the cost of computing for each pixel a least square predictor in a square
block centered on the pixel. This correspondence investigates the reduction of the mathematical
complexity by computing distinct predictors not for pixels, but for groups of pixels. The same
predictors are recovered at detection. Experimental results for the case of prediction on the
rhombus defined by the four horizontal and vertical neighbors are provided. It is shown that by
computing a predictor for a pair of pixels, the computational cost is halved without any loss in

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performance. A small loss appears for groups of three and four pixels with the advantage of
reducing the mathematical complexity to a third and a fourth, respectively.
S30013 - LEARNING ANALYTICS SYSTEM FOR ASSESSING STUDENTS'
PERFORMANCE QUALITY AND TEXT MINING IN ONLINE COMMUNICATION
A challenging and demanding task for the teachers and researchers in e-learning
environments is the assessment of students' performance. This paper is to present a new Learning
analytics system for Learning Management Systems (LMS), that will aid and support teachers
and researchers to understand and analyze interaction patterns and knowledge construction of the
participants involved in ongoing online interactions. It is seamlessly integrated into Moodle.
Learning Management Systems (LMS) does not include analytics tool for comprehensive audit
logs of students' activities and log analysis capabilities interactions, also lack of good evaluation
of participatory level and support for assessment of students' performance quality on LMS.
Semantic similarity measures of text play an increasingly important role in text related research
and applications in tasks such as text mining, web page retrieval, and dialogue systems. Existing
methods for computing sentence similarity have been adopted from approaches used for
Messages texts in LMS. The system enables one to measure semantic similarity between texts
exchanged during communication sessions, in order to find out the degree of coherence in a
discussion tread. It is given as a value of relevance in numerical format.