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Due to the increasing amount of Web sites offering features to contribute rich content and the frequent failure of Web developers to properly sanitize user input, cross-site-scripting prevails as the most significant security threat to Web applications. Using cross-site scripting techniques, a malicious user can hijack Web sessions, craft credible phishing sites and using the browser based exploits can have complete access to victim machine. Previous work towards protecting against cross-site scripting attacks suffers from various drawbacks, such as practical infeasibility of deployment due to the need for client-side modifications, inability to reliably detect all injected scripts, and complex, error-prone parameterization. In this paper, we introduce a server-side solution for detecting and preventing cross-site scripting attacks using reverse proxy that intercepts all HTML responses, and allow or deny the request based on filtering techniques using regular expressions and blacklisting techniques.

HTTP header filtering, Regular expression, Reverse proxy , XSS, XSS firewall

[1] “DOM Based Cross Site Scripting or XSS of the Third Kind” (WASC writeup), Amit Klein, July 2005
[2] Cross Site Scripting Definiton ,Web application Vulnerabilities Wikipedia.
[3] http://www.cgisecurity.com/xss-faq XSS attacks.
[4] Mattison Ward, “Using A Reverse Proxy To Filter HTTP and HTTPS” , GIAC Security Essentials Certification (GSEC), 2012
[5] XSS payloads, OWASP Cheat Sheet for xss attacks.
[6] XSS prevention Rules,OWASP rules for XSS.

K.S. Wagh, Vishal Jotshi, Harshal Dalvi, Manish Kamble, "Reverse Proxy Based XSS filtering," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.175-180, 2015.

Now a day’s prediction systems are key software systems in business and social contexts. We observe the change in decision making and success obtained by organization and individuals due to accurate prediction systems. Various algorithms and theories are used by researchers to formulate prediction systems. We feel the study of state of art methods for design of prediction systems is highly necessary. This work studies the mathematical approaches used for prediction. We propose the application of Rough Set Theory for design of career prediction system.

RST(Rough Set Theory), Accuracy, Prediction System.

[1] Xinjuan Zhang, Jing Zhang; Research on the Graduate Career Decision-making Based on Rough Set Theory and Decision Tree Method.
[2] Niharika Upadhyay and Pragati Jain; APPLYING ROUGH SET THEORY IN FEEDBACK ANALYSIS ;eISSN 2278-1145.
[3] Xinjuan Zhang, Jing Zhang; Research on the Graduate Career Decision-making Based on Rough SetTheory and Decision Tree Method.
[4] Aboul Ella Hassanien, Jafar H. AliRough Set Approach for Generation ofClassification Rules of Breast Cancer Data.
[5] Hameed Al Qaheri, Ajith Abraham; A Generic Scheme for Generating PredictionRules Using Rough Set.
[6] Zdzislaw Pawlak; Rough set theoryand its applications.
[7] Kein E. Voges; Research Techniques Derived From Rough Sets Theory:Rough Classification and Rough Clustering
[8] Sivia Rissino, Germano Lambert Torres; Rough Set Theory – Fundamental Concepts,Principals, Data Extraction, and Applications
[9] Chien Chung Chan, Dept. Of mathematics and computer science; Rough Sets Theory and Its Applications
[10] Zdzisław Pawlak; Rough Sets; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 100 Gliwice, Poland.
[11] Lokesh S. katore, Dr.(Prof). Umale Sir; Novel approach to recommend career option to engineering students using improved analytics.
[12] Xuecai Bao, Tangsheng Wang, Jianfeng Hu;Analysis of Individual Career Decision-making based on partial least-squares regression model” 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
[13] Lokesh S. katore, Dr. J.S. Umale; Comparative Study of Recommendation Algorithms and Systems using WEKA; ISBN: 973-93-80884-86-4
[14] Sudheep Elayidom, Dr. Sumam Mary Idikkula, Joseph Alexander, Anurag Ojha, Applying Data mining techniques for Placement chance prediction” 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies
[15] Ying Cao, Lei Zhang; Research about the College Students Career Counseling Expert System based on Agent”
[16] Jyi-Shane Liu and Ke-Chih Ning; Applying Link Prediction to Ranking Candidates for High-Level Government Post” 2011 International Conference on Advances in Social Networks Analysis and Mining
[17] Bert w. westbrook, Eleanor e. Sanford, Predictive and Construct Validity of Six Experimental Measures of Career Maturity” Journal of Vocational Behavior 27, 338-355 (198.5)
[18] Eliza Byington *, Will Felps, Why do IQ scores predict job performance?An alternative, sociological explanation” Research in Organizational Behavior 30 (2010) 175–202.
[19] Mercedes Inda 1, Carmen Rodríguez ⁎, José Vicente Peña, Gender differences in applying social cognitive career theory in engineering students” Journal of Vocational Behavior 83 (2013) 346–355
[20] Thomas Zellweger ⁎, Philipp Sieger, Frank Halter; Should I stay or should I go? Career choice intentions of students with family business background” Journal of Business Venturing 26 (2011) 521–536

Lokesh S. Katore, Bhakti S. Ratnaparkhi, Jayant S. Umale and Niharika Upadhyay, "Study & Analysis of Statistical Methods and Their Application to Career Prediction System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.181-186, 2015.

Classification is one of the important steps in data mining for categorizing huge amount of data. Different classifiers are in use today for the classification of large data sets. Some classifiers have shown better performance than the others. Though these classifiers have proven better than others, there is still a chance for improvement in the classification process. This improvement can be considered in terms of selecting the important or rather the features that affect most in the classification process. Thus, here we focus on using a set of five attribute selection techniques applied on two classifiers to show how attribute selection affects classification performance. We compare and discuss two well-known classification schemes – MultiLayer Perceptron and Simple Logistics based on the application of these five attribute selection techniques. The aim of this study is to demonstrate and understand the behaviour of these classifiers once subjected to attribute selection schemes.

MultiLayer Perceptron, Simple Logistics, Attribute Selection Techniques, Subset Evaluation, Weka

[1] Malay Mitra and R. K. Samanta, “Cardiac Arrhythmia Classification Using Neural Networks with Selected Features”, International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) (2013).
[2] K. Rajeswari, RohitGarud and V. Vaithiyanathan, “Improving Efficiency of Classification using PCA and Apriori based Attribute Selection Technique”, Research Journal of Applied Sciences, Engg. and Technology 6(24): 4681-4684, (2013).
[3] Mark Hall and Geoffrey Holmes, “Benchmarking Attribute Selection Techniques for Discrete Class Data Mining”, Department of Comp. Science, The University of Waikato 2002.
[4] Trilok Chand Sharma, Manoj Jain, “WEKA Approach for Comparative Study of Classification Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 4, April (2013).
[5] JasminaNovaković, PericaStrbac, DusanBulatović, “Toward Optimal Feature Selection Using Ranking Methods and Classification Algorithms”, Yugoslav Journal of Operations Research 21 (2011).
[6] B.Azhagusundari, Antony SelvadossThanamani, “Feature Selection based on Information Gain”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-2, Jan (2013).
[7] JasminaNovakovic, “Using Information Gain Attribute Evaluation to Classify Sonar Targets”, 17th Telecommunications forum TELFOR (2009).
[8] PhayungMeesad, PudsadeeBoonrawd and VatineeNuipian, “A Chi-Square-Test for Word Importance Differentiation in Text Classification”, International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011).
[9] S. K. Shevade and S. S. Keerthi, “A simple and efficient algorithm for gene selection using sparse logistic regression”, 10.1093/bioinformatics/btg308 pg. 2246–2253 Vol. 19 no. 17 (2003).
[10] Datasets available at the UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/datasets.html.
[11] WEKA,an Open Source software freely available at http://www.cs.waikato.ac.nz/.
[12] AbdulhamitSubasi, Ergun Ercelebi, “Classification of EEG signals using neural network and logisticregression”, Computer Methods and Programs in Biomedicine 78, 87—99 (2005).

Aniket G. Meshram, K. Rajeswari and V. Vaithiyanathan, "Enhancing Classification Performance with Subset and Feature Selection Schemes," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.187-191, 2015.

CAPTCHA is a security mechanism used to prevent bots from using the services of the website intended for humans. Till date, a number of CAPTCHA schemes have been successfully broken which made the design of CAPTCHAs an interesting area of research. Schemes of CAPTCHAs can be categorized as text based, image based, animation based, natural language based, option based and audio based. This paper explains some of the strengths and weaknesses of the CAPTCHA currently used by Yahoo and steps to crack it automatically. The CAPTCHA is an animation based text CAPTCHA. It is cracked by first removing the noise in the background and finally applying our own developed Optical Character Recognition (OCR) program which is specialized for reading characters in Yahoo CAPTCHA only. The automatic crack program has a successful rate of 63%.

Breaking Yahoo CAPTCHA; Animation Based; Text Based; OCR

[1] L.von Ahn, M. Blum, N. J. Hopper and J. Langford, “CAPTCHA: using hard AI problems for security”, Springer, vol. 2656, 2003, pp. 294-311.
[2] J.Yan and A. S. E. Ahmad. “Breaking visual CAPTCHAs with naive pattern recognition algorithms”, IEEE Computer Society, 2007, pp. 279-91.
[3] K.Chellapilla, K. Larson, P. Y. Simard and M. Czerwinski, “Designing human friendly human interaction proofs (HIPs)”, ACM, 2005, pp. 711-720.
[4] J.Yan and A. S. E. Ahmad, “A low-cost attack on a Microsoft CAPTCHA”, ACM conference on computer and communications security,ACM, 2008, pp. 543-554.
[5] A.S.E. Ahmad, J. Yan and W. Y. Ng, “CAPTCHA design: color, usability, and security”, IEEE Internet Computing, 2012, pp. 44-51.
[6] Y.Nakaguro, M. N. Dailey, S. Marukatat and S. S. Makhanov, “Defeating line-noise CAPTCHAs with multiple quadratic snakes”, Computers & Security, Elsevier, 2013, pp. 91-110.
[7] G.Mori and J. Malik, “Recognizing objects in adversarial clutter: breaking a visual CAPTCHA”, IEEE Computer Society, 2003, pp. 134-144.
[8] J.Yan and A. S. E. Ahmad, “CAPTCHA security: a case study”, IEEE Security and Privacy, 2009, pp. 22-28.
[9] J.Yan and A. S. E. Ahmad, “CAPTCHA robustness: a security engineering perspective”, IEEE Computer Society, 2011, pp. 54-60.
[10] G.Moy, N. Jones, C. Harkless and R. Potter, “Distortion estimation techniques in solving visual CAPTCHAs”, IEEE Computer Society conference on Computer Vision and Pattern Recognition, 2004, pp. 23-28.
[11] S.Li, S. A. H. Shah, M. A. U. Khan, S. A. Khayam, A. R. Sadeghi, R. Schmitz, “Breaking e-banking CAPTCHAs”, Proceedings of the 26th Annual Computer Security Applications Conference, 2010, pp. 171-180.
[12] P.Baecher, N. Buscher, M. Fischlin and B. Milde, “Breaking reCAPTCHA: a holistic approach via shape recognition”, IFIP advances in information and communication technology, 2011, pp. 56-67.
[13] C.Cruz-Perez, O. Starostenko, F. Uceda-Ponga, V. A. Aquino and L. Reyes-Cabrera, “Breaking reCAPTCHAs with unpredictable collapse: heuristic character segmentation and recognition”, Springer, 2012, pp. 155-165.
[14] H.Gao, W. Wang, J. Qi, X. Wang, X. Liu, J. Yan, “The robustness of hollow CAPTCHAs”, ACM conference on computer and communications, 2013, pp. 1075-1086.
[15] B.B. Zhu, J. Yan, Q. Li, C. Yang, J. Liu, N. Xu, M. Yi and K. Cai, “Attacks and design of image recognition CAPTCHAs”, ACM conference on computer and communications security, 2010, pp. 187-200.
[16] C.J. Hernandez-Castro, A. Ribagorda, “Pitfalls in CAPTCHA design and implementation: the math CAPTCHA, a case study”, Computers and Security, Elsevier Advanced Technology, 2010, pp. 141-157.
[17] J.Tam, J. Simsa, S. Hyde, L. von Ahn, “Breaking audio CAPTCHAs”, Advances in Neural Information Processing Systems 21, 2008, pp. 1625-1632.
[18] E.Bursztein, R. Beauxis, H. Paskov, D. Perito, C. Fabry, J. C. Mitchell, “The failure of noise-based non-continuous audio CAPTCHAs”, IEEE symposium on security and privacy, 2011, pp. 19-31.

Niket Choudhary, and Sudarshan Deshmukh, "Automatic crack of Yahoo CAPTCHA," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.92-195, 2015.

the capacity of the cloud is allowing users to deploy complex infrastructure on the cloud due to huge pool of the resources offered by the cloud. In the infrastructure as service virtual machines are provided to the end users as the mean of the resource from the cloud. Complex application such as load balancing system or cluster system requires multiple virtual machines and their special connectivity for system realization. Current cloud providers do not allow users to deploy their complex workload. Users will need to deploy these virtual machines manually by selecting one at a time. In this paper we are proposing hint based virtual placement which accepts hints from the users and select the nodes from cloud. This system act as middleware between cloud provider and users and automate the complex workload deployment process. This system mutually understands the constraints placed by both users and cloud admins while deploying virtual machines. Such complex virtual machine deployment can be passed to the system using constraint like AssociateVM, DistantVM, BackupVM and PrivateNetVM. Cloud admins can also place constraints like out of service, backup node etc to influence the virtual machine placement. Our virtual machine scheduler considers these constraints from both the ends and selects the appropriate nodes on the basis of the constraints and scores assigned to the nodes. We have compared this strategy with available known cloud scheduler algorithms and found that available strategies do not allow any user passed hints while our algorithm allow users to pass such hints while deploying virtual machines.

Public Cloud; Virtual Machine Placement; Resource allocation; User Hints

[1] Konstantinos Tsakalozos, Mema Roussopoulos, and Alex Delis "Hint-Based Execution of Workloads in Clouds with Nefeli" IEEE transactions on parallel and ditributed systems, vol. 24, no. 7, July 2013
[2] Bobroff, N. ; T.J. Watson Res. Center, IBM, Hawthorne, NY ; Kochut, A. ; Beaty, K. "Dynamic Placement of Virtual Machines for Managing SLA Violations" Integrated Network Management, 2007. IM '07. 10th IFIP/IEEE
[3] Chaisiri, S. ; Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Bu-Sung Lee ; Niyato, D. "Optimal virtual machine placement across multiple cloud providers" Services Computing Conference, 2009
[4] Remesh Babu, K.R. ; Dept. of Inf. Technol., Gov. Eng. Coll., Idukki, India ; Samuel, P. "Virtual Machine Placement for Improved Quality in IaaS Cloud" Advances in Computing and Communications (ICACC), 2014
[5] Jamali, S. ; Dept. of Electr. & Comput. Eng., Univ. of Mohaghegh Ardebili, Ardebil, Iran ; Malektaji, S. "Improving grouping genetic algorithm for virtual machine placement in cloud data centers" Computer and Knowledge Engineering (ICCKE), 2014
[6] Ricardo Stegh Camati, Alcides Calsavara, Luiz Lima Jr. "Solving the Virtual Machine Placement Problem as a Multiple Multidimensional Knapsack Problem" ICN 2014 : The Thirteenth International Conference on Networks
[7] Nskinc study on cloud computing, Nskinc white paper 2012
[8] Linlin Wu ; Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia ; Garg, S.K. ; Versteeg, S. ; Buyya, R. "SLA-Based Resource Provisioning for Hosted Software-as-a-Service Applications in Cloud Computing Environments" Services Computing, IEEE Transactions on (Volume:7 , Issue: 3 )
[9] Dong Huang ; Inst. for Infocomm Res., Agency for Sci. Technol. & Res, Singapore, Singapore ; Bingsheng He ; Chunyan Miao "A Survey of Resource Management in Multi-Tier Web Applications
[10] Pankaj R Kadam, Nilesh V Alone "Review on KVM Hypervisor" International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-3 Issue-4 September, 2014
[11] Mell, P. and Grance, T. 2011. "The NIST Definition of Cloud Computing" NIST Special Publication, 800-145

Pankaj Kadam and Nilesh Alone, "Hint Based Virtual Machine Placement in Cloud," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.196-202, 2015.

An artificial neural network (ANN) or simply “neural net” is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the human brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. This paper presents a brief introduction to convolutional neural network(CNN) – a neural network with a special structure and describes how it works to recognize handwritten digits.After a network was trained by training dataset from MNIST database, it can classify 10,000 examples from MNIST testing dataset within 35 secondsand achieve3.25% error rate.

Neural Network, Convolutional Neural Network, Feed forward, Back propagation, Classification

[1] Michael Nielsen, “Neural networks and deep learning”, Sep. 2014, http://neuralnetworkanddeeplearning.com.
[2] Patrice Y. Simard, Dave Steinkraus, John C. Platt, “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis”, Microsoft Research, 2003
[3] MNIST Database of Handwritten digits, MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges, 28 Jan, 2015
[4] Y. LeCun, “Generalization and Network Design Stategies”, Technical Report, 1989.
[5] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-Based Learning Applied to Document Recognition”, Proceedings of the IEEE, Vol-86, Issue-11, Page no (2278-2324), 1998

Loc Thanh Huynh, Hung Thang Phung andToai QuangTon, "Using Convolutional Neural Network to Recognize Handwritten Digits," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.203-206, 2015.

Cloud computing is the delivery of computing and storage capacity as a service to users. The cloud has huge potential when it comes to storing, sharing and exchanging files, but the security provided by cloud services is questionable. Users, after uploading their files, have no control anymore about the way their data is handled and the location where it is stored. Considering both corporate and personal data which is often secret and sensitive in nature, one should not blindly entrust it to a cloud storage provider. In order to ensure the confidentiality of date during transferring it to the cloud storage, we need some encryption techniques. In this paper, we present a cryptographic file system based on Multiple-Key Public-Key Cryptography which is designed for enhancing the cloud data storage security for an organization. Here, files are encrypted by the administrator of the organization before they are uploaded to the cloud storage providers. Therefore, the cloud storage providers can’t access the users’ data. This encryption technique also allows the sharing files within a group of users of an organization. A key feature is that it handles changes in group membership and modification of files in an extremely efficient manner, by using the same encryption method.

Dynamic Membership, Multiple Key public key Cryptography, Cloud Storage

[1] Istv´An L´Am., Szilveszter Szebeni., Levente Butty., (2012), “Tresorium: Cryptographic File System For Dynamic Groups Over Untrusted Cloud Storage”, Proceedings of the 41st International Conference on Parallel Processing 14, pp.1594–1603.
[2] Peng Yong., Zhao Wei., Xie Feng., Dai Zhong-Hua., Gao Yang., Chen Dong-Qing.,(2012), ”Secure Cloud Storage Based On Cryptographic Techniques” Proceedings of the IEEE INFOCOM, 14, pp.1594–1603.
[3] I. Lam., S. Szebeni., And L. Buttyan., (2012) “Invitation-Oriented Tgdh: Keymanagement For Dynamic Groups In An Asynchronous Communication Model,” In Submitted To 4th International Workshop On Security In Cloud Computing, 15, 17, PP.1684–1695
[4] V. Sriram., G. Narayan,, and K. Gopinath,, (2007) “SAFIUS - A secure and accountable filesystem over untrusted storage,” In Storage Workshop, 2007. SISW ’07. Fourth International IEEE, pp. 34–45.
[5] D. Grolimund., L. Meisser., S. Schmid., and R. Wattenhofer.,(2006) “Cryptree:A folder tree structure for cryptographic file systems,” In Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems, pp. 189–198.

Blessy Paul V and Ms. Jibi K George , "Cryptographic File System for Secured Group Communication," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.207-211, 2015.

Cloud services provide great conveniences for the users to enjoy the on-demand cloud applications without considering the local infrastructure limitations. During the data accessing, different users may be in a collaborative relationship, and thus data sharing becomes significant to achieve productive benefits. The existing security solutions mainly focus on the authentication to realize that a user’s privative data cannot be unauthorized accessed, but neglect a subtle privacy issue during a user challenging the cloud server to request other users for data sharing. The challenged access request itself may reveal the user’s privacy no matter whether or not it can obtain the data access permissions. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize the public key based homomorphic authenticator and uniquely integrate it with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.

Cloud Computing, Authentication Protocol, Privacy Preservation, Shared Authority, Universal Composability

[1]. Mishra, R. Jain, and A. Durresi, “Cloud Computing: Networking and Communication Challenges,” IEEE Communications Magazine, vol. 50, no. 9, pp, 24-25, 2012.
[2]. R. Moreno-Vozmediano, R. S. Montero, and I. M. Llorente,“Key Challenges in Cloud Computing to Enable the FutureInternet of Services,” IEEE Internet Computing,[online] ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6203493, 2012.
[3]. K. Hwang and D. Li, “Trusted Cloud Computing with Secure Resources and Data Coloring,” IEEE Internet Computing, vol. 14,no. 5, pp. 14-22, 2010.
[4]. J. Chen, Y. Wang, and X. Wang, “On-Demand Security Architecture for Cloud Computing,” Computer, vol. 45, no. 7, pp. 73-78,2012.
[5]. Y. Zhu, H. Hu, G. Ahn, and M. Yu, “Cooperative Provable Data Possession for Integrity Verification in Multi-cloud Storage,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no, 12, pp.2231-2244, 2012.
[6]. H. Wang, “Proxy Provable Data Possession in Public Clouds,”IEEE Transactions on Services Computing, [online] ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6357181, 2012.
[7]. K. Yang and X. Jia, “An Efficient and Secure Dynamic AuditingProtocol for Data Storage in Cloud Computing,” IEEE Transactions on Parallel and Distributed Systems, [online] ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6311398, 2012.
[8]. Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, “Enabling Public Auditability and Data Dynamics for Storage Security in CloudComputing,” IEEE Transactions on Parallel and Distributed Systems,vol. 22, no. 5, pp. 847-859, 2011.
[9]. C. Wang, K. Ren, W. Lou, J, Lou,“Toward Publicly Auditable Secure Cloud Data Storage Services,” IEEE Network, vol. 24, no.4, pp. 19-24, 2010.
[10]. L. A. Dunning and R. Kresman, “Privacy Preserving Data Sharing With Anonymous ID Assignment,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 2, pp. 402-413, 2013.
[11]. X. Liu, Y. Zhang, B. Wang, and J. Yan, “Mona: Secure MultiOwner Data Sharing for Dynamic Groups in the Cloud,” IEEE Transactions on Parallel and Distributed Systems, [online] ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6374615, 2012.
[12]. S. Grzonkowski and P. M. Corcoran, “Sharing Cloud Services:User Authentication for Social Enhancement of Home Networking,”IEEE Transactions on Consumer Electronics, vol. 57, no. 3, pp.1424-1432, 2011.
[13]. Y. Xiao, C. Lin, Y. Jiang, X. Chu, and F. Liu, “An Efficient Privacy-Preserving Publish-Subscribe Service Scheme for Cloud Computing,” in Proceedings of Global Telecommunications Conference (GLOBECOM 2010), December 6-10, 2010.
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Bullarao Domathoti, Rajia Begum and Nageswara Rao.P, "Privacy Preserving Collaborative Auditing Data Storage Scheme in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.212-218, 2015.

Reversible data hiding technique in which the hidden data property is maintained even after processing on the image. The original cover image remains same after processing on the embedded image, original data is obtained. The image is used to embed additional information in the encrypted images, applies in many fields of security which can be recoverable with original media and the hided data without loss. Some previous methods embed data reversible vacating room from the encrypted images, which matter to error on data extraction and/or image restoration. This system proposes a reversible data hiding technique which work is separable; the receiver can extract embedded data. If a system for lossless compression of images applies a recover step, this step must map integer input values to integer output values. This can be achieved using the integer wavelet transform. This proposed system can achieve the excellent reversibility, that is, data extraction and same image quality. This system is better to handle secret communication in open environment like internet

Reversible Data Hiding, Image Encryption, Data Protection, Integer wavelength Transform

[1] Kede Ma, Weiming Zhang, “Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption” IEEE Transactions On Information Forensics And Security, Vol. 8, No. 3, March 2013
[2] Zhicheng Ni, Yun-Qing Shi, “Reversible Data Hiding” IEEE Transactions On Circuits And Systems For Video Technology, March 2006.
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[4] Mohammad Ali Alavianmehr, Mehdi Rezaei Mohammad Sadesgh Helfroush, “A Semi-Fragile Lossless Data Hiding Scheme Based on Multi-level Histogram Shift in Image Integer Wavelet Transform Domain.
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[7] J. Tian, “Reversible data embedding using a difference expansion,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890–896, Aug. 2003.
[8] W. Hong, T. Chen, and H.Wu, “An improved reversible data hiding in Encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199–202, Apr. 2012.
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Nitin Shelake and S. R. Durugkar , "Extracting Hidden Data from Encrypted Images Using IWT," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.219-222, 2015.

Robustness of face recognition systems are measured by its ability to overcome the problem of changing in facial expression and rotation of individuals’ face images. This paper represents a face recognition system that overcomes the problem of changes in facial expressions in three-dimensional (3D) range images. We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. A novel perception inspired non-metric partial similarity measure is introduced, which is potentially useful in deal with the concerned problems because it can help capturing the prominent partial similarities that are dominant in human perception. The effectiveness of the proposed method in handling large expressions, partial occlusions and other distortions is demonstrated on several well-known face databases.

SOM, 3D face

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Vengatesh R and Rajbarath, "Robust 3D Face Recognition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.223-226, 2015.