Abstract

The major aims of this technique a secure multi-owner information sharing theme. It implies that any user within the cluster will firmly share information with others by the world organization trust worthy cloud. This theme is ready to support dynamic teams.
Efficiently, specifically, new granted users will directly rewrite information files uploaded before their participation whereas not contacting with information owners. User revocation are simply achieved through a very distinctive revocation list whereas not modification the key. Keys of the remaining users the scale and computation overhead of cryptography square measure constant and freelance with the number of revoked users. We’ve a bent to gift a secure and privacy-preserving access management to users that guarantee any member throughout a cluster to anonymously utilize the cloud resource. Moreover, the important identities of information owners are disclosed by the cluster manager once disputes occur. We offer rigorous security analysis, and perform intensive simulations to demonstrate the potency of our theme in terms of storage and computation overhead. Cloud computing provides a cost-effective degreed economical resolution for sharing cluster resource among cloud users sharing information associate degree passing throughout a} terribly multi-owner manner whereas protecting information an identity privacy from an international organisation faithful cloud continues to be a hard issue, because of the frequent modification of the membership.

Abstract

Neural Network with image and data processing techniques was employed to implement an automated Prostate Histopathology tumor classification. The conventional method for Prostate Histopathology images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also nonreproducible.
Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of neural networks shown great potential in this field. Using the optimal texture features extracted from normal and tumor regions of Prostate Histopathology by using statistical features, BPN and RBF classifiers are used to classify and segment the tumor portion in abnormal images. Both the testing and training phase gives the percentage of accuracy on each parameter in neural networks, which gives the idea to choose the best one to be used in further works. The performance of the NN classifier was evaluated in terms of training performance and classification accuracies. Neural Network gives fast and accurate classification than other neural networks and it is a promising tool for classification of the tumors.

Abstract

Rough c-means algorithmic rule has gained increasing attention in recent years. However, the assignment scheme of Rough c-means algorithmic rule doesn’t incorporate any info concerning the neighbours of the info purpose to be appointed and may cause undesirable solutions in apply. This paper proposes associate extended Rough c-means agglomeration algorithmic rule supported the concepts of decision-theoretic Rough Sets model. Within the risk calculation, a replacement quite loss function is employed to capture the loss info of the neighbours. The assignment theme of the current multi-category decision-theoretic Rough Sets model is additionally adjusted to deal with the doubtless high procedure value. Experimental results are provided to validate the effectiveness of the projected approach.

Abstract

Internet become further modern inside the day to day activity’s of user’s. In recent years on-line social networks (OSN) collectively increased quickly. The user’s can communicate and share their views and content through on-line social networking services (OSN). The sharing between the users have to be compelled to be many types of content like image, audio, video etc. the foremost draw-back of these on-line Social Networking (OSN) services is that the dearth of privacy for the user’s own personal house. The users can’t have the ability to direct management to forestall the unsought messages denote on their own personal walls. On-line Social Networks (OSN) becomes an important a vicinity of the many folks life of late. Therefore on-line Social Networks (OSN) have to be compelled to be very secured to forestall the individual’s privacy. Up to presently information superhighway Social Network (OSN) provides the security measures unit restricted. To filter the unwanted messages, throughout this paper we’ve got a bent to planned associate exaggerated filtering system by victimization machine learning technique supported a content filtering.

Abstract

This paper presents a novel traffic classification scheme to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.

Abstract

A large quantity of digital visual information is being distributed and communicated globally and therefore the question of video internal control becomes a central concern. in contrast to several signal process applications, the supposed receiver of Video signals are nearly forever the human eye. Video quality assessment algorithms should plan to assess sensory activity degradation in videos. My thesis focuses on full reference strategies of image and video quality assessment, wherever the provision of an ideal or pristine reference image/video is assumed. An outsized body of analysis on image quality assessment has centered on models of the human sensory system. Recent approaches to image quality assessment, the structural similarity index and knowledge hypothetical models, avoid express modeling of visual mechanisms and use applied mathematics properties derived from the photographs to formulate measurements of image quality.
Motion info plays a key role in perception of video signals. I develop a general, spatial spectral localized multi scale framework for evaluating dynamic video fidelity that integrates each spatial and temporal aspects of distortion assessment. Video quality is evaluated in house and time by evaluating motion quality on computed motion trajectories. Mistreatment this framework, I develop a fullreference video quality assessment algorithmic program referred to as the motion-based Video Integrity analysis index, or show index.
The information contains a hundred and fifty distorted videos obtained from ten representational reference videos and every video was evaluated by thirty eight human subjects within the study. I study the performance of leading, in public on the market objective video quality assessment algorithms on this information.

Abstract

Current approaches to enforce fine-grained access management on confidential information hosted within the cloud area unit supported fine-grained coding of the info. Underneath such approaches, information homeowners area unit answerable of encrypting the info before uploading them on the cloud and re-encrypting the info whenever user credentials modification.
Information homeowners therefore incur high communication and computation prices. a stronger approach ought to delegate the enforcement social management} of fine-grained access control to the cloud, so to minimize the overhead at the info homeowners, whereas reassuring information confidentiality from the cloud. We tend to propose associate approach, based on 2 layers of coding, that addresses such demand. Underneath our approach, the info owner performs a coarse-grained encryption, whereas the cloud performs a fine-grained coding on prime of the owner encrypted information. A difficult issue is however to decompose access management policies (ACPs) such the 2 layer coding are often performed. We tend to show that this drawback is NP-complete and propose novel optimisation algorithms. We tend to utilize associate economical cluster key management theme that supports expressive ACPs. Our system assures the confidentiality of the info and preserves the privacy of users from the cloud whereas delegating most of the access management social control to the cloud.

Abstract

A projected proxy-based multi cloud computing framework permits dynamic, on the fly collaborations and resource sharing among cloud-based services, addressing trust, policy, and solitude issues whereas not pre established collaboration agreements or standardized interfaces. The recent rush in cloud computing arises from its ability to supply package, infrastructure, and platform services whereas not requiring outsized investments or expenses to manage and operate them. Clouds typically involve service suppliers, infrastructure/resource suppliers, and repair users (or clients). They embody applications delivered as services, additionally as a result of the hardware and package systems providing these services. Cloud computing characteristics embody a gift (network-based) access channel resource pooling multitenancy automatic and elastic provisioning and incaution of computing capabilities and metering of resource usage (typically on a pay-per-use basis). Virtualization of resources like processors, network, memory, and storage ensures quality and high convenience of computing capabilities. Clouds can dynamically provision these virtual resources to hosted applications or to purchasers that use them to develop their own applications or to store data. Quick provisioning and dynamic reconfiguration of resources make possible address variable demand and guarantee optimum resource utilization.

Abstract

Image fusion has attracted a widespread attention attributable to applications in medical imaging, automotive and remote sensing. Image fusion deals with group action knowledge obtained from completely different sources of data for intelligent systems.
Image fusion provides output as one image from a collection of input pictures obtained from completely different sources or techniques. {Different totally completely different completely different} approaches in image fusion offer different sort of results for various applications.. We tend to found this system terribly helpful in medical imaging and different areas, wherever quality of image is a lot of necessary than the $64000 time application.
The work is supplemented by algorithms, its simulation and analysis victimization entropy.

Abstract

strongly maintain log report over unlimited periods of your time is exceedingly vital to the right implementation of any institute. Reliability of the monitor files which of the work method ought to be ensured in the slightest degree repeats. Additionally, as log files typically contain sensitive info, confidentiality and privacy of log records square measure equally vital. However, deploying a secure work infrastructure involves substantial capital expenses that a lot of organizations could notice overwhelming. Authorization log management to the cloud seems to be a viable value saving live.
During this paper, we have a tendency to determine the challenges for a safe cloud-based log management service and propose a structure for doing a similar.

Abstract

Cloud-assisted mobile health (mHealth) observation that applies the prevailing mobile communications and cloud computing technologies to manufacture feedback decision support, has been thought-about as a revolutionary approach to raising the standard of health care service whereas lowering the health care worth. Sadly, it to boot poses a significant risk on every purchasers privacy and holding of observation service suppliers that may deter the wide adoption of m Health technology. This paper is to handle this necessary downside and magnificence a cloud-assisted privacy protecting mobile health observation system to safeguard the privacy of the involved parties and their data. Moreover, the outsourcing decipherment technique and a replacement planned key personal proxy re secret writing square measure tailored to shift the procedure quality of the involved parties to the cloud whereas not compromising clients’ privacy and repair providers’ holding.
Finally, our security and performance analysis demonstrates the effectiveness of our planned vogue.

Abstract

Cloud transmission services supply associate economical, flexible, and ascendible process technique and provide a solution for the user demands of high quality and diversified transmission. As intelligent mobile phones and wireless networks become further and a lot of commonplace, network services for users are no longer restricted to the house. Transmission data are obtained merely victimisation mobile devices, allowing users to fancy ubiquitous network services. Considering the restricted system of measurement accessible for mobile streaming and utterly completely different device desires, this study given a network and device-aware Quality of Service (QoS) approach that has transmission data applicable for a terminal unit surroundings via interactive mobile streaming services, additional considering the overall network surroundings and adjusting the interactive transmission frequency and conjointly the dynamic transmission transcoding, to avoid the waste of knowledge live and terminal power. Finally, this study complete a model of this style to validate the utility of the projected technique. in line with the experiment, this system could supply economical self-adaptive transmission streaming services
for various system of measurement environments.

Abstract

Cloud computing is changing into fashionable. Building highquality cloud applications could be a important analysis drawback. QoS rankings offer valuable info for creating best cloud service choice from a group of functionally equivalent service candidates. to get QoS values, real-world invocations on the service candidates area unit sometimes needed. To avoid the long and highpriced 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 alternative shoppers.
Our projected framework needs no further invocations of cloud services once creating QoS ranking prediction. 2 personalized QoS ranking prediction approaches area unit projected to predict the QoS rankings directly. Comprehensive experiments area unit conducted using real-world QoS information, as well as three hundred distributed users and five hundred real world internet services everywhere the globe. The experimental results show that our approaches beat out alternative competitor approaches.

Abstract

During this paper, we advise a privacy-preserving public auditing system for knowledge storage safety in cloud computing. victimization cloud storage, users will tenuously store their knowledge and revel in the on-demand high-quality applications and services from a shared pool of configurable dividing resources, while not the burden of native knowledge storage and preservation. However, the actual fact that users not have physical possession of the outsourced knowledge makes the info integrity protection in cloud computing a tough task, expressly for users with forced computing possessions. Moreover, users ought to be able to simply use the cloud storage as if it’s native, while not distressing concerning the requirement to verify its dependability.
Thus, facultative public auditability for cloud storage is of vital importance in order that users will resort to a third-party auditor (TPA) to see the integrity of outsourced knowledge and be worry free. To firmly introduce a lively TPA, the auditing method ought to usher in no new vulnerabilities toward user knowledge privacy and introduce no any on-line drawback to user. During this paper, we tend to propose a secure cloud storage system supporting privacy-preserving public auditing. We tend to any spread our result to change the TPA to perform audits for multiple users at the same time and with efficiency. General security and performance analysis show the projected schemes square measure demonstrably secure and extremely well-organized. Our primary experiment conducted on Amazon EC2 instance any demonstrates the quick performance of the look.

Abstract

Distributed file systems are key building blocks for cloud computing applications supported the Map cut back programming paradigm. In such file systems, nodes at the equal time serve computing and storage functions; a file is partitioned off into variety of chunks allotted in distinct nodes in order that Map cut back tasks is performed in parallel over the nodes. However, during a cloud computing atmosphere, failure is that the norm and nodes could also be upgraded, replaced, and additional within the system.
Files also can be dynamically created, deleted and appended. This leads to load imbalance during a distributed file system; that’s the file chunks aren’t distributed as uniformly as attainable among the nodes. Rising distributed file systems in production systems powerfully depend upon node. The large-scale, failureprone atmospheres as a result of the central load balancer is anesthetize appreciable work that’s linearly scaled with the system size, and will therefore become the performance bottleneck and also the single purpose of failure. During this paper, a completely distributed load rebalancing rule is conferred to deal with the load imbalance drawback.
Our rule is compared against a centralized approach during a production system and competitor distributed resolution conferred with in the literature. The imitation consequences indicate that our proposal is comparable the present centralized approach and significantly outperforms the previous distributed rule in terms of load imbalance issue, movement value, and algorithmic overhead.

Abstract

Now a days cloud computing plays a key role for sharing group resource among their users. Due to the frequent changes of membership maintaining multi owner data is becoming a difficult task and also sharing of data in an untrusted cloud is also a major challenge.For that purpose we introduce the MONA for dynamic groups in the cloud and it supports for group signature and broadcast encryption techniques.So that any cloud user can share data with the others.Here the revocation list is also presented.

Abstract

Expanding Ring Search (ERS) algorithm is a commonly used method in searching for a route from source to destination. In flooding, a node transmits a message to all of its neighbours. The neighbours in turn transmit a message to all of its neighbours and so on until the message has been propagated to the entire network. HopsPrediction-ERS(HP-ERS) executes an efficient route discovery using history of hop counts and it helps in selecting good TTL value. A good initial TTL value can reduce the number of re-transmitting request messages in the route discovery process.
It checks for a route to the destination in the cache. If no route is found, then it checks for an item to the destination in the hops table. If no valid item is found, flooding is used as a way to propagate route request messages. This may lead to energy consumption because re-broadcasting of packets takes place even though the route is formed or same RREQ packet is received by the intermediate node many times which also consumes energy.
The energy consumption takes place because the nodes send the packets from one node to the other and communicate with each other without any infrastructure.
We propose an algorithm called HP-Blocking-ERS algorithm which reduces the energy consumption. HP-Blocking-ERS works by introducing delay at each ring of TTL. After this delay the intermediate node receives the chase packet containing “stopinstruction” which stops re-broadcasting same packet to the intermediate node. In this thesis, we select the best TTL value and simultaneously we also introduce a chase packet to avoid rebroadcasting of same packet. This controls energy consumption which is the main problem in MANETS and AODV.

Abstract

A fast clustering-based feature selection algorithm (FAST) is proposed and experimentally evaluated as it works in two steps. Initially, features are separated into clusters by using graph-theoretic clustering methods. Secondly, the supreme illustrative feature is intensely allied to target classes is selected from each cluster to form a subset of features. Features in various clusters are rather independent. To ensure the efficiency of FAST, we take on the efficient minimum-spanning tree (MST) clustering method.
The efficacy and use of the FAST algorithm are evaluated through an empirical study. The consequences, on publicly existing real-world high-dimensional image, microarray, and text data, demonstrate that the FAST not only yields minor subsets of features but also progresses the performances of the four types of classifiers.

Abstract

Cloud computing permits business customers to proportion and down their resource usage supported wants, we have a tendency to gift a system that uses virtualization technology to apportion information center resources dynamically supported application demands and support inexperienced computing by optimizing the amount of servers in use. We have a tendency to introduce the conception of “skewness” to live the unevenness within the four-dimensional resource utilization of a server. By minimizing asymmetry, we are able to mix differing kinds of workloads nicely and improve the utilization of server resources. We have a tendency to develop a collection of heuristics that forestall overload within the system effectively whereas saving energy used. Several of the touted gains within the cloud model come back from resource multiplexing through virtualization technology. During this paper Trace driven simulation and experiment results demonstrate that our rule achieves smart performance.

Abstract

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and dataintensive applications without infrastructure investment. Along the processing of that applications a large volume of intermediate data sets will be generated and often stored to save the cost of recomposing them. 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 adopts in existing approaches to address this challenge. 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 proposes 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.

Abstract

Present days mobile devices plays key role in human life. These devices reached a wide development by adding different applications as providing assistance in healthcare with the combination of cloud server. These types of applications will reduce the health care cost and provide good decision support for client .Unfortunately in this we have serious risk in both client and provider’s privacy. To provide privacy for both systems we are using the outsourcing decryption and key private proxy re-encryption.

Abstract

Modern scientific databases and web databases maintain large and heterogeneous data. In real world databases contain over hundreds or even thousands of relations and attributes. Traditional predefine query forms are not able to satisfy various ad hoc queries from users on such databases. This paper proposes DQF a novel database query form interface which is able to dynamically generate query forms. The essence of DQF is to select a user preference and rank query form components assisting him/her to make decisions. The generations of a query form is an iterative process and is guide by the user. At each iteration system automatically generates ranking lists of form components and the user then adds the desired form components into the query form. The ranking of form components are based on the capture user priority. A user can also fill the query form and submit queries to view the query result at each iteration.
In this way a queries form could be dynamically refine till the user satisfies with the query results.

Abstract

In this paper, we have a tendency to study user profile matching with the privacy-preservation in mobile social networks (MSNs) and introduce a family of novel profile matching protocols. We first propose an exact Comparison-based Profile Matching protocol (eCPM) that runs between 2 parties, associate instigator and a respondent. The eCPM allows the instigator to get the comparison-based matching result a few such that attribute in their profiles, whereas preventing their attribute values from disclosure. We then propose associate implicit Comparison-based Profile Matching protocol (iCPM) that permits the instigator to directly obtain some messages rather than the comparison result from the respondent. The messages are unrelated to user profile is divided into multiple classes by the respondent. The instigator implicitly chooses the interested class that is unknown to the respondent. 2 messages in every class area unit ready by the responder, and just one message is obtained by the instigator according to the comparison result on one attribute. We further generalize the iCPM to associate implicit Predicate-based Profile Matching protocol (iPPM) that permits advanced comparison criteria spanning multiple attributes.
The namelessness analysis shows all these protocols come through the confidentiality of user profiles. In addition, the eCPM reveals the comparison result to the instigator and provides solely conditional anonymity; the iCPM and also the iPPM don’t reveal the result in the least and supply full namelessness. We analyze the communication overhead and also the namelessness strength of the protocols. We have a tendency to then gift associate increased version of the eCPM, referred to as eCPM+, by combining the eCPM with a novel prediction-based reconciling anonym modification strategy. The performance of the eCPM and also the eCPM+ area unit relatively studied through in depth to the trace-based simulations.

Abstract

Proposal of a fault node recovery algorithm to improve the epoch of a wireless sensor network when some of the sensor nodes shut down. The algorithm is created on the grade diffusion algorithm collective with the genetic algorithm. The algorithm can consequence in rarer substitutes of sensor nodes and additional reprocessed routing paths. The proposed algorithm upsurges the amount of active nodes up to 8.7 times, decreases the rate of data loss by approximately 98.8%, and diminishes the rate of energy consumption by approximately 31.1%.K

Abstract

Images are generally the collection of pixels which is used in different areas such as science, engineering and so on. With the increase use of digital techniques for transmit and sorting images, the essential issue of protecting the integrity, confidentiality as well as the authenticity of images has become a major disquiet.
Classical cryptographic algorithms such as RSA, DES and AES are inefficient for image encryption due to image innate features, particularly high volume data. In this paper, we try to implement Image encryption using S-DES (Simplified Data Encryption Standard) based on Chaotic Logistic map (Arnold cat-map). In preceding work, most researchers used to make a chaotic icon using a key and then encrypt the chaotic image using the same key, but in this paper, first make a chaotic map of the image using Arnold cat-map, this chaotic map developed using a simple chaotic purpose, in this process there is no key will be used. Then use that chaotic image as a key for encrypting the image using S-DES.
Combining the chaotic map with S-DES system can enhance the security of system by using the characteristic of sensibility of original value and randomness in chaotic map. Thus in this paper select the key when encrypt the image and used a chaotic image as a key not any other text. Thus the encryption speed is some faster in this implementation as compare to previous work.

Abstract

As we know, today everyone is using On-line Social Networks (OSNs) to communicate and share information. Therefore one important need in today On-line Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. OSNs provide little support to this requirement up to now. To provide this, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is accomplished through a flexible rule-based system, which allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier which automatically produce membership labels in support of content-based filtering.

Abstract

Single nucleotide polymorphisms are one of the major causes of genetic diseases. So, identification of disease causing SNPs can pave way for better disease diagnosis. Hence, the present study aims at identification of detrimental SNPs in TACR1 gene.

Abstract

Clustering is an unsupervised learning technique used to place data elements into related groups without advance knowledge of the group definitions. Among various types of clustering techniques, K-Means is one of the most popular algorithms.
The objective of K-Means clustering algorithm is to make the distances of objects in the same cluster as small as possible. In this paper a new hybrid K-Means algorithm is implemented by modification of both the phases of original k-means algorithm. Results show that the new algorithm gives best results for all performance parameters than original k-means algorithm and enhanced algorithm. Although the proposed algorithm solves the problem of accuracy and better clusters are produced to some extent, more hybrid algorithm in the future can be developed from previous enhancements.

Abstract

Wireless Networks are susceptible for a variety of malicious attacks because they use shared transmission medium. The transmission of information may be jammed by an attacker by introducing malicious packets into the network. These jammers create a lot of noise in the total network and create problems at both the ends of transmission and affect the performance of the wireless networks. Hence in every wireless network employ certain techniques to detect or to avoid or to prevent jamming attacks. Jammers deny services to the authorized users by jamming legitimate traffic by illegitimate traffic. An attacker sends unauthorized packets to the nodes in the network and increase the traffic to jam the network.
The jammers purposefully interfere with the physical transmission and reception of the wireless communication. The jamming attacks do not comply with the MAC protocols. The impact of an unintentional disruption can be minimized by identifying the presence of the jamming attacks and by implementing an appropriate prevention methods. Jammers can be identified in the MAC layer. To prevent these attacks, some schemes such as Triple DES, multilevel steganography. This paper also investigates the solutions to reduce the effectiveness of jammer as well as to decrease the jamming rate.

Abstract

A creative and initial work focused on designing a system to estimate motion sickness occurs during travelling. Motion sickness is a condition in which a disagreement exists between visually perceived movement and the vestibular system’s sense of movement. To reduce possibilities of visually-induced motion sickness caused by animations, video games and movies, there is a need to develop an evaluation method of visually-induced motion sickness. The paper is focused on estimating MSI (Motion Sickness Incidence) from the simulink model for 360 DOF (degrees-of-freedom) in the 3D (dimensional) space. MEMS (Micro Electro Mechanical System) gyroscope based MSI control device is proposed and is implemented. This evaluation system can be applied to early detect the subject’s motion sickness level and prevent the uncomfortable syndromes occurred in advance in our daily life.

Abstract

Secure Broadcasting of data efficiently to a remote co operative group in an emerging network is always a problem. The dynamics of the sender and a fully trusted and reliable key generation is a big challenge where the communication is limited. The key management algorithms that are available are not able to deal with these problems.
The existing paradigms failed to provide better efficiency and security in these kind of transmissions. A major challenge in devising such a system involves in achieving efficient usage of Bandwidth and Reducing the number of unintended receivers. In this paper we circumvent these obstacles and close this gap by involving a sender based algorithm .This new paradigm is a hybrid of traditional Multicasting, shortest path techniques and group key management. In such a system, for every source destination pair the protocol adaptively calculates the mean delays along all the utilized paths and avoid the paths with greater or equal mean delays. Which eventually reduces the usage of unwanted paths and also results in reducing the number of unintended receivers at a considerable rate. This approach efficiently deals with the computation overhead and usage of network resources. Further more our scheme provides better security by reducing the number of unintended receivers.

Abstract

As incremental research in data mining. the process of extraction of data from large database is observed. One well known topic is Privacy preserving data mining (PPDM) which is very essential to maintain a ratio between privacy protection and knowledge discovery. The goal is to hide sensitive item sets so that the adviser cannot extract the modified database. To solve such problems there are some algorithms presented by many authors. Main goal of this survey is to understand the existing privacy preserving data mining techniques and to forward our approach to propose a another well technique to preserve privacy. Main study focus of that survey is on privacy preserving utility mining (PPUM) and presents two novel algorithms, HHUIF and MSICF, to achieve the goal of hiding sensitive item sets so that the adversaries cannot mine them from the modified database. How that work minimizing the impact on the sanitized database of hiding sensitive item sets. The experimental results of reviewed work shows the HHUIF achieves lower miss costs than MSICF on two synthetic datasets..

Abstract

The wireless sensor networks have wide processing capacity in which the data transmission plays a vital role in WSN. We are enhancing a system to have a better data transmission rate.
Based upon the study we found that the transmission of data in the Military is still not highly secure. So our system enhancing the security based on the Digital Signature and the security under digital signature. Initially we group the nodes in the form of cluster and those clusters have the cluster head to access the region. After the formation of the cluster region, need to analyze the mobility, and therefore to process the data transmission. For the transmission of data we propose the algorithm EEDC in the existing they have used LEECH. Being the process in wireless network, clusters are formed periodically and dynamically. In the security part we are using two ways, one is the identity based digital signature and Identity based online/offline Digital Signature. So the system will be much more secure than the present strategy. The attacker can be easily identified with this network.

Abstract

Steganography is going to gain its importance due to the exponential growth and secret communication of potential computer users over the internet. It can also be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. Generally data embedding is achieved in communication, image, text, voice or multimedia content for copyright, military communication, authentication and many other purposes. In this paper we have proposed a different type of steganography techniques overview and also propose the classification and its application.

Abstract

A good number protocols provide security for wireless sensor networks and provide confidentiality to the messages. Even then the contextual information is exposed. The location information has paramount importance in sensor networks. Certain adversaries may derive this information and about the data sinks in the network.
This may reduce the application of any network. One stronger adversary like Eavesdropper override all the existing techniques of prevention. This paper discusses various techniques to provide location privacy for source node and also for sink node. We have discussed two techniques for the prevention of the leakage of location information. They are Source Location Privacy and destination Location Privacy Techniques. It is observed that the proposed techniques are efficient and effective in protecting location information from the attacker.

Abstract

Tracking the position using GPS or other equivalent technologies is an increasing important feature in many applications like surveying, mapping, transportation, agriculture, military planning, GIS and others. The positional accuracy of any place is tend to error due to various factors including poor geometrical position of satellites, weather changes in ionosphere and troposphere layer, multipath effects and so on. This inaccuracy may affect various real time navigational systems and position finding systems.
Map matching algorithms can augment the positional solution by improving the accuracy. When the GPS Trajectory Dataset is matched to digital maps / Satellite maps, it is important to identify the layers of road network to identify the user position exactly. So this study aims in building a prototype system suitable for data collection from GPS receivers, identifying bridges in GPS trajectory data set, establishing map matching integrated with Google Map and Google Earth and analyzing the results.

Abstract

Nowadays, the usage of smart phones has increased tremendously. Every phone requires some type of operating system to run its services. The operating system is responsible for determining the functions and features available on your device. The acceptance of a third party application, so called mobile apps is also based on the mobile operating system. Every manufacturer will have chosen the operating system for that specific device. In order to know the device compatibility and support for the mobile applications, its necessary to learn about the mobile operating system. This paper gives an idea about different mobile operating systems and also a comparative study on mobile operating systems.

Abstract

Cryptography algorithms are either symmetric algorithms, or asymmetric algorithms. Symmetric algorithms work much faster than asymmetric algorithms. But it requires secured mechanism to share keys. Whereas hybrid encryption mechanism gives advantages of both algorithms and avoids disadvantages of both algorithms. Hybrid encryption will give faster performance since plain text will be encrypted using symmetric algorithm and key sharing will be done using asymmetric algorithm. We can use hybrid mechanism for either confidentiality or authentication. In this paper, we will evaluate hybrid encryption used for authentication in distributed systems.

Abstract

Earthquake is a natural phenomenon which occurs and affects almost all parts of the earth. An earthquake is experienced as the shaking of the ground caused by the sudden breaking and movement of large sections i.e. tectonic plates of the earth’s crust. As the earthquake occurs, the energy is released which increases manifold with each unit increase in the magnitude of the earthquake. With the earthquakes, there is another consequence associated which is called as area of rupture. This paper focuses on visually analyzing the energy released and the area of rupture caused due to earthquakes using world’s earthquakes dataset.
Moreover, the paper includes earthquake analysis of the dataset of a particular region on earth using Hidden Markov Model (HMM) , which is one of the powerful tools to be used for earthquake analysis in which the emission of the sequence is observed but does not know the sequence of states the model goes through to generate the emissions. This implementation using HMM toolbox of Matlab to study and analyse the sequence of emissions of varying magnitudes leads to the analysis in the form of a transition diagram and is the main emphasis of the paper.

Abstract

Wireless Sensor Network (WSN) consists of large number of sensor node deployed in an ad-hoc manner. Communication takes place between the source node and sink node with the help of some intermediate node. Various routing protocols have been proposed for the purpose of data transmission. SPIN (Sensor Protocol for Information via Negotiation) is a data centric routing protocol used in WSN which efficiently transmits information between sensor nodes in an energy constrained mode. This paper proposes a modified version of Optimized SPIN named as Location Based Optimized SPIN. The main objective of this protocol is to use the location information for advertising and sending data to the sink.
This reduces the energy consumption of the nodes in transmitting and receiving the data. Implementation of Basic SPIN, Optimized SPIN and proposed Location Based Optimized SPIN protocols will be done using MATLAB.