java and .net ieee 2012

IEEE 2012 Titles & Abstract

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Networks and network security

1. A Distributed Control Law for Load Balancing in Content Delivery Networks

ABSTRACT

In this paper, we face the challenging issue of defining and implementing an effective law

for load balancing in Content Delivery Networks (CDNs). We base our proposal on a formal

study of a CDN system, carried out through the exploitation of a fluid flow model

characterization of the network of servers. Starting from such characterization, we

derive and prove a lemma about the network queues equilibrium. This result is then

leveraged in order to devise a novel distributed and time-continuous algorithm for load

balancing, which is also reformulated in a time-discrete version. The discrete formulation of

the proposed balancing law is eventually discussed in terms of its actual implementation in a

real-world scenario. Finally, the overall approach is validated by means of simulations.

2. TAM: A Tiered Authentication of Multicast Protocol for Ad-Hoc Networks

ABSTRACT

Ad-hoc networks are becoming an effective tool for many mission critical applications such

as troop coordination in a combat field, situational awareness, etc. These applications are

characterized by the hostile environment that they serve in and by the multicast-style of

communication traffic. Therefore, authenticating the source and ensuring the integrity of the

message traffic become a fundamental requirement for the operation and management of

the network. However, the limited computation and communication resources, the large

scale deployment and the unguaranteed connectivity to trusted authorities make known

solutions for wired and single-hop wireless networks inappropriate. This paper presents a

new Tiered Authentication scheme for Multicast traffic (TAM) for large scale dense ad-

hoc networks. TAM combines the advantages of the time asymmetry and the secret

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information asymmetry paradigms and exploits network clustering to reduce overhead and

ensure scalability. Multicast traffic within a cluster employs a one-way hash function chain in

order to authenticate the message source. Cross-cluster multicast traffic includes message

authentication codes (MACs) that are basedon a set of keys. Each cluster uses a unique

subset of keys to look for its distinct combination of valid MACs in the message in order to

authenticate the source. The simulation and analytical results demonstrate the performance

advantage of TAM in terms of bandwidth overhead and delivery delay.

3. Privacy- and Integrity-Preserving Range Queries in Sensor Networks

Abstract—The architecture of two-tiered sensor networks, where storage nodes serve as an

intermediate tier between sensors and a sink for storing data and processing queries, has

been widely adopted because of the benefits of power and storage saving for sensors as well

as the efficiency of query processing. However, the importance of storage nodes also makes

them attractive to attackers. In this paper, we propose SafeQ, a protocol that prevents

attackers from gaining information from both sensor collected data and sink issued queries.

SafeQ also allows a sink to detect compromised storage nodes when they misbehave. To

preserve privacy, SafeQ uses a novel technique to encode both data and queries such that a

storage node can correctly process encoded queries over encoded data without knowing

their values. To preserve integrity, we propose two schemes—one using Merkle hash trees

and another using a new data structure called neighborhood chains—to generate integrity

verification information so that a sink can use this information to verify whether the result of

a query contains exactly the data items that satisfy the query. To improve performance, we

propose an optimization technique using Bloom filters to reduce the communication cost

between sensors and storage nodes.

Wireless Networks

4. Adaptive Opportunistic Routing for Wireless Ad Hoc Networks




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ABSTRACT

A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is

proposed. The proposed scheme utilizes a reinforcement learning framework to

opportunistically route the packets even in the absence of reliable knowledge about channel

statistics and network model. This scheme is shown to be optimal with respect to an

expected average per-packet reward criterion. The proposed routing scheme jointly

addresses the issues of learning and routing in an opportunistic context, where

the network structure is characterized by the transmission success probabilities. In

particular, this learning framework leads to a stochastic routing scheme that optimally

“explores” and “exploits” the opportunities in the network.

5. Local Greedy Approximation for Scheduling in Multihop Wireless Networks

ABSTRACT

In recent years, there has been a significant amount of work done in developing low-

complexity scheduling schemes to achieve high performance in multihop wireless networks.

A centralized suboptimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a

good candidate because its empirically observed performance is close to optimal in a variety

of network settings. However, its distributed realization requires high complexity, which

becomes a major obstacle for practical implementation. In this paper, we develop simple

distributed greedy algorithms for scheduling in multihop wireless networks. We reduce the

complexity by relaxing the global ordering requirement of GMS, up to near zero. Simulation

results show that the new algorithms approximate the performance of GMS, and outperform

the state-of-the-art distributed scheduling policies.

Mobile computing

6. Toward Reliable Data Delivery for Highly Dynamic Mobile Ad Hoc Networks




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ABSTRACT

This paper addresses the problem of

delivering data packets for highly dynamic mobile ad hoc networks in areliable and timely

manner. Most existing ad hoc routing protocols are susceptible to node mobility,

especially for large-scale networks. Driven by this issue, we propose an efficient Position-

based Opportunistic Routing (POR) protocol which takes advantage of the stateless property

of geographic routing and the broadcast nature of wireless medium. When a data packet is

sent out, some of the neighbor nodes that have overheard the transmission will serve as

forwarding candidates, and take turn to forward the packet if it is not relayed by the specific

best forwarder within a certain period of time. By utilizing such in-the-air backup,

communication is maintained without being interrupted. The additional latency incurred by

local route recovery is greatly reduced and the duplicate relaying caused by packet reroute

is also decreased. In the case of communication hole, a Virtual Destination-based Void

Handling (VDVH) scheme is further proposed to work together with POR. Both theoretical

analysis and simulation results show that POR achieves excellent performance even under

high node mobility with acceptable overhead and the new void handling scheme also works

well.

7. Distributed Throughput Maximization in Wireless Networks via Random

Power Allocation

ABSTRACT

We develop a distributed throughput-optimal power allocation algorithm in wireless

networks. The study of this problem has been limited due to the nonconvexity of the

underlying optimization problems that prohibits an efficient solution even in a centralized

setting. By generalizing the randomization framework originally proposed for input queued

switches to SINR rate-based interference model, we characterize the throughput-optimality

conditions that enable efficient and distributed implementation. Using gossiping algorithm,

we develop a distributed power allocation algorithm that satisfies the optimality conditions,




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thereby achieving (nearly) 100 percent throughput. We illustrate the performance of our

power allocation solution through numerical simulation.

8. Hop-by-Hop Routing in Wireless Mesh Networks with Bandwidth Guarantees

ABSTRACT

Wireless Mesh Network (WMN) has become an important edge network to provide Internet

access to remote areas and wireless connections in a metropolitan scale. In this paper, we

study the problem of identifying the maximum available bandwidth path, a fundamental

issue in supporting quality-of-service in WMNs. Due to interference among links, bandwidth,

a well-known bottleneck metric in wired networks, is neither concave nor additive in wireless

networks. We propose a new path weight which captures the available path bandwidth

information. We formally prove that our hop-by-hop routing protocol based on the new path

weight satisfies the consistency and loop-freeness requirements. The consistency property

guarantees that each node makes a proper packet forwarding decision, so that a data

packet does traverse over the intended path. Our extensive simulation experiments also

show that our proposed path weight outperforms existing path metrics in identifying high-

throughput paths.

Wireless Sensor Networks

9. On the Throughput Capacity of Wireless Sensor Networks with Mobile Relays

ABSTRACT

In wireless sensor networks (WSNs), it is difficult to achieve a large data collection rate

because sensors usually have limited energy and communication resources. Such an issue is

becoming more and more challenging with the emerging of information-intensive

applications that require high data collection rate. To address this issue, in this paper, we

investigate the throughput capacity of WSNs where multiple mobile relays are deployed to




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collect data from static sensors and forward them to a static sink. To facilitate the

discussion, we propose a new mobile relay assisted data collection (MRADC) model.

Based on this model, we analyze the achievable throughput capacity of largescale WSNs

using a constructive approach, which can achieve a certain throughput by choosing

appropriate mobility parameters. Our analysis illustrates that, if the number of relays is less

than a threshold, then the throughput capacity can be increased linearly with more

relays. On the other hand, if the number is greater than the threshold, then the throughput

capacity becomes a constant, and the capacity gain over a static WSN depends on two

factors: the transmission range and the impact of interference. To verify our analysis, we

conduct extensive simulation experiments, which validate the selection of mobility

parameters, and which demonstrate the same throughput behaviors obtained by analysis.

Knowledge and Data Mining

10. Publishing Search Logs—A Comparative Study of Privacy Guarantees

ABSTRACT

Search engine companies collect the “database of intentions,” the histories of their users'

search queries. These search logs are a gold mine for researchers. Search engine

companies, however, are wary of publishing search logs in order not to disclose sensitive

information. In this paper, we analyze algorithms for publishing frequent keywords,

queries, and clicks of a search log. We first show how methods that achieve variants of k-

anonymity are vulnerable to active attacks. We then demonstrate that the stronger

guarantee ensured by ε-differential privacy unfortunately does not provide any utility for this

problem. We then propose an algorithm ZEALOUS andshow how to set its parameters to

achieve (ε, δ)-probabilistic privacy. We also contrast our analysis of ZEALOUS with an

analysis by Korolova et al. [17] that achieves (ε',δ')-indistinguishability. Our paper concludes

with a large experimental study using real applications where we compare

ZEALOUS and previous work that achieves k-anonymity in search log publishing. Our results




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show that ZEALOUS yields comparable utility to k-anonymity while at the same time

achieving much stronger privacy guarantees.

11. Efficient Multi-dimensional Fuzzy Search for Personal Information

Management Systems

ABSTRACT

With the explosion in the amount of semistructured data users access and store in personal

information management systems, there is a critical need for powerful search tools to

retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically

support some IR-style ranking on the textual part of the query, but only consider structure

(e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a

novel multidimensional search approach that allows users to perform fuzzy searches for

structure and metadata conditions in addition to keyword conditions. Our techniques

individually score each dimension and integrate the three dimension scores into a

meaningful unified score. We also design indexes and algorithms to efficiently identify the

most relevant files that match multidimensional queries. We perform a thorough

experimental evaluation of our approach and show that our relaxation and scoring

framework for fuzzy query conditions in noncontent dimensions can significantly improve

ranking accuracy. We also show that our query processing strategies perform and scale well,

making our fuzzy search approach practical for every day usage.

12. Prediction of User's Web-Browsing Behavior: Application of Markov Model

ABSTRACT

Web prediction is a classification problem in which we attempt to predict the next set of Web

pages that a user may visit based on the knowledge of the previously visited pages.

Predicting user's behavior while serving the Internet can be applied effectively in various




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critical applications. Such application has traditional tradeoffs between modeling

complexity and prediction accuracy. In this paper, we analyze and study Markov

model andall-$K$th Markov model in Web prediction. We propose a new modified Markov

model to alleviate the issue of scalability in the number of paths. In addition, we present a

new two-tier prediction framework that creates an example classifier EC, based on the

training examples and the generated classifiers. We show that such framework can improve

the prediction time without compromising prediction accuracy. We have used standard

benchmark data sets to analyze, compare, and demonstrate the effectiveness of our

techniques using variations of Markov models and association rule mining. Our experiments

show the effectiveness of our modified Markov model in reducing the number of paths

without compromising accuracy. Additionally, the results support our analysis conclusions

that accuracy improves with higher orders of all-$K$th model.

13. A Probabilistic Scheme for Keyword-Based Incremental Query Construction

ABSTRACT

Databases enable users to precisely express their informational needs using structured

queries. However, database query construction is a laborious and error-prone process, which

cannot be performed well by most end users. Keyword search alleviates the usability

problem at the price of query expressiveness. As keyword search algorithms do not

differentiate between the possible informational needs represented by a keyword query,

users may not receive adequate results. This paper presents IQP - a novel approach to bridge

the gap between usability of keyword search and expressiveness of database queries.

IQP enables a user to start with an arbitrary keyword query and incrementally refine it into a

structured query through an interactive interface. The enabling techniques of IQP include: 1)

a probabilistic framework for incremental query construction; 2) a probabilistic model to

assess the possible informational needs represented by a keyword query; 3) an algorithm to

obtain the optimal query construction process. This paper presents the detailed design of

IQP, and demonstrates its effectiveness and scalability through experiments over real-world

data and a user study.




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14. Ranking Model Adaptation for Domain-Specific Search

Abstract—With the explosive emergence of vertical search domains, applying the broad-

based ranking model directly to different domains is no longer desirable due to domain

differences, while building a unique ranking model for each domain is both laborious for

labeling data and time-consuming for training models. In this paper, we address these

difficulties by proposing a regularization based algorithm called ranking adaptation SVM (RA-

SVM), through which we can adapt an existing ranking model to a new domain, so that the

amount of labeled data and the training cost is reduced while the performance is still

guaranteed. Our algorithm only requires the prediction from the existing ranking models,

rather than their internal representations or the data from auxiliary domains. In addition, we

assume that documents similar in the domain-specific feature space should have consistent

rankings, and add some constraints to control the margin and slack variables of RA-SVM

adaptively. Finally, ranking adaptability measurement is proposed to quantitatively estimate

if an existing ranking model can be adapted to a new domain. Experiments performed over

Letor and two large scale datasets crawled from a commercial search engine demonstrate

the applicabilities of the proposed ranking adaptation algorithms and the ranking

adaptability measurement.

15. Slicing: A New Approach to Privacy Preserving Data Publishing

ABSTRACT

Several anonymization techniques, such as generalization and bucketization, have been

designed for privacy preserving microdata publishing. Recent work has shown that

generalization loses considerable amount of information, especially for high-dimensional

data. Bucketization, on the other hand, does not prevent membership disclosure and does

not apply for data that do not have a clear separation between quasi- identifying attributes

and sensitive attributes. In this paper, we present a novel technique called slicing, which




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partitions the data both horizontally and vertically. We show that slicing preserves better

data utility than generalization and can be used for membership disclosure protection.

Another important advantage of slicing is that it can handle high-dimensional data. We show

how slicing can be used for attribute disclosure protection and develop an efficient algorithm

for computing the sliced data that obey the ℓ-diversity requirement. Our workload

experiments confirm that slicing preserves better utility than generalization and is more

effective than bucketization in workloads involving the sensitive attribute. Our experiments

also demonstrate that slicing can be used to prevent membership disclosure.

16. Data Mining Techniques for Software Effort Estimation: A Comparative

Study

A predictive model is required to be accurate and comprehensible in order to inspire

confidence in a business setting. Both aspects have been assessed in a software effort

estimation setting by previous studies. However, no univocal conclusion as to which

technique is the most suited has been reached. This study addresses this issue by

reporting on the results of a large scale benchmarking study. Different types of techniques

are under consideration, including techniques inducing tree/rule-based models like M5 and

CART, linear models such as various types of linear regression, nonlinear models (MARS,

multilayered perceptron neural networks, radial basis function networks, and least squares

support vector machines), and estimation techniques that do not explicitly induce a model

(e.g., a case-based reasoning approach). Furthermore, the aspect of feature subset selection

by using a generic backward input selection wrapper is investigated. The results are

subjected to rigorous statistical testing and indicate that ordinary least squares regression in

combination with a logarithmic transformation performs best. Another key finding is that by

selecting a subset of highly predictive attributes such as project size, development, and

environment related attributes, typically a significant increase in estimation accuracy can be

obtained.

17. Ranking and Clustering Software Cost Estimation Models through a Multiple

Comparisons Algorithm




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Software Cost Estimation can be described as the process of predicting the most realistic

effort required to complete a software project. Due to the strong relationship of accurate

effort estimations with many crucial project management activities, the research community

has been focused on the development and application of a vast variety of methods and

models trying to improve the estimation procedure. From the diversity of methods emerged

the need for comparisons to determine the best model. However, the inconsistent results

brought to light significant doubts and uncertainty about the appropriateness of the

comparison process in experimental studies. Overall, there exist several potential sources of

bias that have to be considered in order to reinforce the confidence of experiments. In this

paper, we propose a statistical framework based on a multiple comparisons algorithm in

order to rank several cost estimation models, identifying those which have significant

differences in accuracy and clustering them in non-overlapping groups. The proposed

framework is applied in a large-scale setup of comparing 11 prediction models over 6

datasets. The results illustrate the benefits and the significant information obtained through

the systematic comparison of alternative methods.

18. Using Linked Data to Annotate and Search Educational Video Resources for

Supporting Distance Learning

Title and Guide

Abstract—Multimedia educational resources play an important role in education, particularly

for distance learning environments. With the rapid growth of the multimedia web, large

numbers of educational video resources are increasingly being created by several different

organizations. It is crucial to explore, share, reuse, and link these educational resources for

better e-learning experiences. Most of the video resources are currently annotated in an

isolated way, which means that they lack semantic connections. Thus, providing the facilities

for annotating these video resources is highly demanded. These facilities create the

semantic connections among video resources and allow their metadata to be understood




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globally. Adopting Linked Data technology, this paper introduces a video annotation and

browser platform with two online tools: Annomation and SugarTube. Annomation enables

users to semantically annotate video resources using vocabularies defined in the Linked

Data cloud. SugarTube allows users to browse semantically linked educational video

resources with enhanced web information from different online resources. In the prototype

development, the platform uses existing video resources for the history courses from the

Open University (United Kingdom). The result of the initial development demonstrates the

benefits of applying Linked Data technology in the aspects of reusability, scalability, and

extensibility.

Cloud Computing

19. Scalable and Secure Sharing of Personal Health Records in Cloud

Computing using Attribute-based Encryption

ABSTRACT

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 party servers and to unauthorized 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 semi-trusted 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




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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 users. A high degree of patient privacy is guaranteed simultaneously by

exploiting multi-authority 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.

20. Enabling Secure and Efficient Ranked Keyword Search over Outsourced

Cloud Data

Cloud computing economically enables the paradigm of data service outsourcing. However,

to protect dataprivacy, sensitive cloud data have to be encrypted before outsourced to the

commercial public cloud, which makes effective data utilization service a very challenging

task. Although traditional searchable encryption techniques allow users to

securely search over encrypted data through keywords, they support only

Booleansearch and are not yet sufficient to meet the effective data utilization need that is

inherently demanded by large number of users and huge amount of data files in cloud. In

this paper, we define and solve the problem

of secureranked keyword search over encrypted cloud data. Ranked search greatly

enhances system usability by enablingsearch result relevance ranking instead of sending

undifferentiated results, and further ensures the file retrieval accuracy. Specifically, we

explore the statistical measure approach, i.e., relevance score, from information retrieval to

build a secure searchable index, and develop a one-to-many order-preserving mapping

technique to properly protect those sensitive score information. The resulting design is able

to facilitate efficient server-sideranking without losing keyword privacy. Thorough analysis

shows that our proposed solution enjoys “as-strong-as-possible” security guarantee

compared to previous searchable encryption schemes, while correctly realizing the goal

of ranked keyword search. Extensive experimental results demonstrate the efficiency of the

proposed solution.




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21. An Efficient and

Secureynamic Auditing Protocol for Data Storage in Cloud Computing

ABSTRACT

In cloud computing, data owners host their data on cloud servers and users

(data consumers) can access thedata from cloud servers. Due to the data outsourcing,

however, this new paradigm of data hosting service also introduces new security challenges,

which requires an independent auditing service to check the data integrity inthe cloud.

Some existing remote integrity checking methods can only serve for static archive data and

thus cannot be applied to the auditing service since the data in the cloud can be

dynamically updated. Thus, an efficient and secure dynamic auditing protocol is desired to

convince data owners that the data are correctly stored in the cloud. In this paper, we first

design an auditing framework for cloud storage systems and propose an efficient and

privacy-preserving auditing protocol. Then, we extend our auditing protocol to support

the data dynamic operations, which is efficient and provably secure in the random oracle

model. We further extend our auditing protocol to support batch auditing for both multiple

owners and multiple clouds, without using any trusted organizer. The analysis and

simulation results show that our proposed auditing protocols are secure and efficient,

especially it reduce the computation cost of the auditor.

22. Towards Secure and Dependable Storage Services in Cloud Computing.

Cloud storage enables users to remotely store their data and enjoy the on-demand high

quality cloud applications without the burden of local hardware and software management.

Though the benefits are clear, such a service is also relinquishing users' physical possession

of their outsourced data, which inevitably poses new security risks toward the correctness of

the data in cloud. In order to address this new problem and further achieve a secure and

dependable cloud storage service, we propose in this paper a flexible distributed storage

integrity auditing mechanism, utilizing the homomorphic token and distributed erasure-


http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6311398&contentType=Early+Access+Articles&queryText%3DEfficient+Audit+Service+Outsourcing+for+Data+Integrity+in+Clouds

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6311398&contentType=Early+Access+Articles&queryText%3DEfficient+Audit+Service+Outsourcing+for+Data+Integrity+in+Clouds



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coded data. The proposed design allows users to audit the cloud storage with very

lightweight communication and computation cost. The auditing result not only ensures

strong cloud storage correctness guarantee, but also simultaneously achieves fast data error

localization, i.e., the identification of misbehaving server. Considering the cloud data are

dynamic in nature, the proposed design further supports secure and efficient dynamic

operations on outsourced data, including block modification, deletion, and append. Analysis

shows the proposed scheme is highly efficient and resilient against Byzantine failure,

malicious data modification attack, and even server colluding attacks.

23. Ensuring Distributed Accountability for Data Sharing in the Cloud

Abstract—Cloud computing enables highly scalable services to be easily consumed over

the Internet on an as-needed basis. A major feature of the cloud services is that users’ data

are usually processed remotely in unknown machines that users do not own or operate.

While enjoying the convenience brought by this new emerging technology, users’ fears of

losing control of their own data (particularly, financial and health data) can become a

significant barrier to the wide adoption of cloud services. To address this problem, in this

paper, we propose a novel highly decentralized information accountability framework to

keep track of the actual usage of the users’ data in the cloud. In particular, we propose an

object-centered approach that enables enclosing our logging mechanism together with

users’ data and policies. We leverage the JAR programmable capabilities to both create a

dynamic and traveling object, and to ensure that any access to users’ data will trigger

authentication and automated logging local to the JARs. To strengthen user’s control, we

also provide distributed auditing mechanisms. We provide extensive experimental studies

that demonstrate the efficiency and effectiveness of the proposed approaches.

Information Forensics and Security

24. A Novel Data Embedding Method Using Adaptive Pixel Pair Matching


http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206



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This paper proposes a new data-hiding method based on pixel pair matching (PPM). The

basic idea of PPM is touse the values of pixel pair as a reference coordinate, and

search a coordinate in the neighborhood set of thispixel pair according to a given message

digit. The pixel pair is then replaced by the searched coordinate to conceal the digit.

Exploiting modification direction (EMD) and diamond encoding (DE) are two data-

hidingmethods proposed recently based on PPM. The maximum capacity of EMD is 1.161

bpp and DE extends the payload of EMD by embedding digits in a larger notational system.

The proposed method offers lower distortion than DE by providing more compact

neighborhood sets and allowing embedded digits in any notational system. Compared with

the optimal pixel adjustment process (OPAP) method, the proposed method always has

lower distortion for various payloads. Experimental results reveal that the

proposed method not only provides better performance than those of OPAP and DE, but also

is secure under the detection of some well-known steganalysis techniques.


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