iosr-JCE   Volume-1 ~ Issue-6

Abstract: Over the past decade more and more users of the Internet rely on the search engines to help them find the information they need. However the information they find depends to a large extent, on the ranking mechanism of the search engines they use. Not surprisingly it in general consists of a large amount of information that is completely irrelevant. Text summarization is a process of reducing the size of a text while preserving its information content. Text Summarization is an emerging technique for understanding the main purpose of any kind of documents. To visualize a large text document within a short duration and small area like PDA screen, summarization provides a greater flexibility and convenience. This research focuses on developing a statistical automatic text summarization approach, K-mixture probabilistic model, to enhancing the quality of summaries. Sentences are ranked and extracted based on their semantic relationships significance values. The objective of this research is thus to propose a statistical approach to text summarization.
Keywords - Extraction, Keywords, Statistical approach, Text Summarization, Webpage.

[1] JIANG Xiao-YU," Improving the Performance of Text Categorization using Automatic Summarization", International Conference on Computer Modeling and Simulation 978-0-7695-3562-3/09 ©2009 IEEE.
[2] Khushboo S. Thakkar," Graph –Based Algorithms for Text Summarization", Third International Conference on Emerging Trends in Engineering and Technology 978-0-7695-4246-1/10©2010IEEE.
[3] Munesh Chandra," A Statistical approach for Automatic Text Summarization by Extraction"
2011 International Conference on Communication Systems and Network Technologies 978-0-7695-4437-3/11©2011 IEEE.
[4] LiChengcheng,"Automatic Text Summarization Based on Rhetorical Structure Theory", 2010 International Conference on Computer Application and System Modeling 978-1-4244-7237-6©2010 IEEE.
[5] Jagdish S KALLIMANI," Information Retrieval by Text Summarization for an Indian Regional Language",978-1-4244-6899-7/10@2010IEEE.
[6] Tengfei Ma,"Multi Document Summarization Using Minimum Distortion", 2010 IEEE International Conference on Data Mining 1550-4786/10©2010 IEEE.
[7] ZHANG Pei-ying," Automatic Text Summarization based on sentences clustering and extraction", 978-1-4244-4520-2/09©2009 IEEE.
[8] Celal Cigir,"Generic Text Summarization for Turkish", 978-1-4244-5023-7/09©2009 IEEE.
[9] Md.MohsinAli,"Multi-document Text Summarization: SimWithFirst Based Features and Sentence Co-selection Based Evaluation", 2009 International Conference on Future Computer and Communication 978-0-7695-3591-3/09©2009 IEEE.

Abstract: fingerprint authentication is implemented using many algorithms, which are based either on minutiae
analysis or non minutiae analysis. Preprocessing of the fingerprints is important for better accuracy.
Preprocessing includes segmentation and noise removal, which are considered two major steps in
preprocessing. In this paper segmentation is performed by using fuzzy logic system, as the edges of the finger
print plays a major role in the authentication fuzzy logic analysis improves sharpness of edges. This
preprocessed image is taken as the input, and then fingerprint authentication is performed on two modules
analysis on matching percentage is determined.
Keywords: authentication minutiae, segmentation preprocessing, matching percentage

[1] J. Yin, X. Yang, G. Zhang, C. Hun, "Two steps for fingerprint Segmentation," Image and Vision Computing, Vol. 25, pp. 1391–1403,2007.
[2] L. O. Chua, L. Yang. "Cellular neural networks: theory and Applications," IEEE trans. circuits and system, Vol. 35, pp. 1257-1290, 1988.
[3] L. O. Chua. "CNN: a vision of complex," Int. J. bifurcation and chaos, Vol. 7, No. 10, pp. 2219-2425, 1997.
[4] L. O. Chua, T. Roska. Cellular neural networks and visual computing. Cambridge Press, London, 2002.
[5] M. Yao. Digital image processing ( In Chinese ). Mechanical Industry Publishing House, Beijing, 2006.
[6] Ursula Gonzales-Barron, Francis Butler. "A comparison of seven threshold techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis," Journal of Food Engineering, Vol. 74, pp. 268–278, 2006.
[7] N. Otsu. "A threshold selection method for grey level histograms," IEEE Trans. Syst. Man Cybern, SMC-9 (1), pp. 62–66, 1979.

Abstract: In this paper, we are introducing a blind authentication method which is based on the secret sharing technique with a data repair capability for document images with the use of the PNG image. We generate an authentication signal for each block of a document image which, together with the block content in binary form, is transformed into several shares using the Shamir secret sharing scheme. These parameters are carefully chosen so that a large number of shares possible are generated and embedded into an alpha channel plane. Now the alpha channel plane is combined with the original image to form a PNG image. In the process of image authentication, an image block is marked as tampered if the authentication signal computed from the current block content does not match the one extracted from the shares embedded in the alpha channel plane. Repairing of data is now done to each tampered block by a reverse Shamir technique after collecting any two or more shares from unmarked blocks. Also a measure to protect the security of the data hidden in the alpha channel is proposed.
Index Terms: Image authentication, secret sharing, data repair, data hiding, and PNG (Portable Network Graphics) image.

[1] A Secret-Sharing-Based Method For Authentication Of Grayscale Document Images Via The Use Of The PNG Image With A Data Repair Capability By Che-Wei Lee, And Wen-Hsiang Tsai,At IEEE Transactions On Image Processing, Vol. 21, No. 1, January 2012.
[2] A Geometry-Based Secret Image Sharing Approach By Chien-Chang Chen And Wen- Yin Fu Department Of Computer Science Hsuan Chuang University Hsinchu, 300 Taiwan Journal Of Information Science And Engineering 24, 1567-1577(2008).
[3] Secret Sharing And Information Hiding By Shadow Images Chin-Chen Chang,The Duc Kieu Department Of Information Engineering And Computer Science, Feng Chia University, Taichung 40724, Taiwan, R.O.C.
[4] Secret Image Sharing With Steganography And Authentication Chang-Chou Lin, Wen-Hsiang Tsai,Department Of Computer And Information Science, National Chiao Tung University, Hsinchu 300,Taiwan,ROC Received 24 October 2002;Received In Revised Form 30 May 2003;Accepted 20 July 2003.
[5] Improvements Of Image Sharing With Steganography And Authentication Ching-Nung Yang , Tse- Shih Chen, Kun Hsuan Yu, Chung-Chun Wang Department Of Computer Science And Information Engineering, National Dong Hwa University, Sec. 2, Da Hsueh Rd., Hualien, Taiwan Received 22 October 2005; Received In Revised Form 18 November 2006; Accepted 26 November 2006.
[6] W. H. Tsai, "Moment-Preserving Thresholding:
A New Approach," Comput. Vis. Graph. Image Process. Vol. 29, No. 3, Pp. 377–393, Mar.1985.
[7] A. Shamir, "How To Share A Secret," Commun.ACM,Vol.22, No.11,Pp.612–613,Nov. 1979.
[8] M. Wu And B. Liu, "Data Hiding In Binary Images For Authentication And Annotation," IEEE Trans.On Multimedia Vol.6,No. 4, Pp.528–538, 2004.
[9] H. Yang And A. C. Kot, "Binary Image Authentication With Tampering Localization By Embedding Cryptographic Signature And Block Identifier," IEEE Signal Processing Letters, Vol.13, No. 12,Pp. 741–744, Dec. 2006.
[10] H. Yang And A. C. Kot, "Pattern-Based Data Hiding For Binary Images Authentication By Connectivity-Preserving," IEEE Trans. On Multimedia, Vol. 9, No. 3, Pp. 475–486, April 2007.
[11] C. H. Tzeng And W. H. Tsai. "A New Approach To Authentication Of Binary Images For Multimedia Communication With Distortion Reduction And Security Enhancement," IEEE Communications Letters, Vol. 7, No. 9, Pp. 443–445.

Abstract: Normally the ratio of population versus physician is so feeble in most of the economically under-developed and developing countries that cause delayed diagnosis and non-availability of on-time treatment of heart patients specifically in the rural areas. It forces to think about availability of a low cost equipment in the rural health centres which may readily diagnose and alarm the critical cardiac condition of patient in absence of cardiologist too, so that the patient can immediately be carried to nearby urban health centre for early treatment. This paper presents an effective and handy PCG device which is equally useful for urban health centres to assist the cardiologist in making faster decision and control of the ailment, which is vital and critical subject in treating such patients. The equipment developed is low cost, quiet affordable by anyone or the rural health centres, portable and easy to maneuver by anyone who possess even a little knowledge to handle it and read the analysis. It does not require any specialized medical person to use the equipment. The diagnosis is carried through computer-based inference system which counts for reliability and accuracy of the diagnosis.
Keywords – Phonocardiograph, PCG device, Human Heart, Osculation, Spectral Analysis,

[1] A. Mahabuba, J. Vijay Ramnath and G. Anil, Analysis of heart sounds and cardiac murmurs for detecting cardiac disorders using phonocardiography, Journal of Instrumentation Society of India, Vol. 39 No.1, March 2009.
[2] Shivajirao M. Jadhav, Sanjay L. Nalbalwar & Ashok A. Ghatol, Modula Neural Network based Arrhythmia Classification System using ECG Signal Data., International Journal of Information Technology and Knowledge Management, January – June 2011, Vol. 4, pp. 205 – 209.
[3] Gokhan Bilgin and Oguz Altun, Cardiac Problem Diagnosis with Satistical Neural Networks and Performancee Evaluation by ROC (Receiver Operation Characteristics), Yildiz Technical University, Istanbul.
[4] Curt G. DeGroff, Sanjay Bhatikar, Jean Hertzberg, Robin Shandas, Lilliam Valdes-Crutz, and Roop L. Mahajan, Artificial Neural Network-Based Method of Screening Heart Murmurs in Children, Circulation, Jounal of American Heart Association, Circulation 2001;103;2711-2716.
[5] Shaikh Abdul Hannan, R. R. Manza, and R. J. Ramteke, Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis, International Journal of Computer Applications (0975-8887), Volume 7 – No. 13, October 2010.
[6] Sepideh Babei and Amir Garanmayeh, Heart Sound Reproduction based on Neural Network classification of Cardiac Valve disorders using wavelet Transformation of PCG Signals, Computers in Biology and Medicine 39 (2009) 8-15, Elsevier.
[7] Ganmin Ning, Jie Su, yingqi Li, Xiaoying Wang, Chenghong Li, Weimin Yan, amd Xioxinang Zheng, Artificial Neural Network based Model for Cardiovascular Risk Stratification in Hypertension, Medical Biological Engineering Computation (2006) 44; 202-208, DIO 10, 1007/s11517-006-0028-2.
[8] Christer Ahlstorm, Processing of the Phonocardiographic signal – method for the intelligent stethoscope. Linkoping University, Institute of Technology, Thesis No: 1253, LIU-TEK-LIC: 2006: 34.
[9] Durand, L.G., de Guise, J., and Guardo, R.A.L.: A microcomputer-based spectrum analyser for phnocardiography. Proceedings of the 32nd Ani.ual Conference on Engineering in Medicine and Biology, vol. 21, p. 2, October 1979.
[10] Kingsley, B.: Acoustic Evaluation of Prosthetic Cardiac Valve in the Audio Spectrum. Journal of Audio Engineering Society Vol. 20, No. 9, pp. 750-755, November 1972.

Abstract: Heterogeneous features of thyroid nodules in ultrasound images is very difficult task when radiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, uses the groups Benign (non-cancerous) and Malignant (cancerous) Thyroid Nodules images were used. The texture feature method like GLCM are very useful for classifying texture of images and these features are used to train the classifiers such as SVM, KNN and Bayesian. The experimental result shows the performance of the classifiers and shows the best predictive value and positively identify the percentage of the non-cancerous or cancerous people and shows the best performance accuracy using the SVM classifier as compare to the KNN and Bayesian classifier.
Keywords- Thyroid Ultrasound (US) images, Feature extraction, GLCM, RBAC, SVM, KNN and Bayesian.

[1] Mary C. Frates, Carol B.Benson, "Management of thyroid nodules detected at US: society of radiologists in ultrasound consensus conference statement" radiology 2005.
[2] D.E Maroulis, M.A Savelonas, S.A Karkanis, D.K. Iakovidis, N.Dimitropoulos, "Computer- aided thyroid nodule detection in ultrasound images,'' proc 18th IEEE Symposium on computer based medical system, 2005
[3] Liujie "SVM-KNN Discriminative Nearest Neighbour Classification for Visual Category Recognition" Proceeding of the 2006 IEEE (CVPR'06). [
4] Shawn Lankton, Allen Tannenbaum "Localizing Region-Based Active Contours" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 17, NO. 11, NOVEMBER 2008. Published in final edited form as:IEEE Trans Image Process. 2008 November; 17(11): 2029–2039.
[5] Yueyi I. Liu "Bayesian classifier for differentiating malignant and benign nodules using sonography features" AMIA 2008 symposium proceeding page-419.
[6] Chuan-Yu Chang, "Classification of the thyroid nodules using SVM", Neural network, 2008. (IEEE world congress on computational 2008.
[7] A. Uppuluri. GLCM Texture Features,http://www.mathworks.com/matlabcentral/fileexchange/22187-glcmtexture- features.
[8] Maria E. Lyra, Nefeli Lagopati, paraskevi charalabatou, Efrosini vasoura, Aristides, costas georgosopoulos, "Texture characterization in Ultrasound of the thyroid gland". Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE
[9] P. Babaghorbani, S. Parvaneh "sonography images for breast cancer texture classification in diagnosis of malignant or benign tumours" IEEE 2010.
[10] Eystratios G. Keramidas "TND: A thyroid nodule detection system for analysis of ultrasound images and videos." Springer 2010.
[11] D.Selvathi, V.S.Sharnitha "Thyroid classification and segmentation in ultrasound imaging using machine learning algorithm" international conference on signal processing, communication, computing and network techniques (ICSCCN) 2011.
[12] Ms. Nikita Singh, Mrs Alka Jindal "A Survey of Different types of Characterization Technique in Ultra sonograms of the Thyroid Nodules" Published in international journal for computer science and informatics volume 1 issue 4. 2012.

Abstract : Working with Optical Character Recognition for the printed Gurmukhi Script is a challenging task due to the large number of characters, the sophisticated ways in which they combine, and the complicated result. This paper describes a fast and easy to implement algorithm for detection of bold and italic character in Gurmukhi Script. The algorithm works without recognition of actual character and detects the font style (bold or italic) in the way of weight and slope. The procedure of identification and classification of bold and italic character can be used to improve character recognition. This simple and fast algorithm gives high accuracy.
Keywords - OCR, Noice, Pixel, Font type phase, Binarization.

[1] Lehal, G. S., Singh, C. and Ritu Lehal, "A Shape Based Post Processor for Gurmukhi OCR" Department of Computer Science and Engineering, Punjabi University, Patiala, India. Vol. 12, NO. 2, pp. 2-12 (1999).
[2] Lehal, G. S. and Chandan Singh, "A Gurmukhi script recognition system", in Proceedings IS'" International Conference on Pattern Recognition, Vol 2, pp. 557-560 (2000).
[3] Rajiv K. Sharma & Dr. Amardeep Singh, "Segmentation of Handwritten Text in Gurmukhi Script". International Journal of Image Processing, Volume (2): Issue (3)
[4] Anand Arokia Raj, Kishore Prahallad, "Identification and Conversion of Font-Data in Indian Languages" at International Conference on Universal Digital Library (ICUDL2007) November 2007, Pittsburgh, USA.Albert Visser, Discourse Representation by Hypergraphs, November 6, 2001
[5] Garain, U. and Chaudhuri, B. B. "Extraction of Type Style Based Meta-Information from Imaged Documents" Computer Vision & Pattern Recognition Unit Indian Statistical Institute Calcutta 700 035, INDIA Proc. 15th Int. Conf. on Pattern Recognition (ICPR), Vol. 2, pp. 610- 612, 1999.
[6] Zramdini, A. and Ingold, R. "Optical Font Recognition Using Typographical Features," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 8, pp.877-882, 1995.
[7] Zhang, L. , Lu, Y. and Tan, C. L. "Italic font recognition using stroke pattern analysis on Wavelet decomposed word images". In ICPR ‟04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR‟04) Volume 4, pages 835– 838, Washington, DC, USA, 2004. IEEE Computer Society.
[8] Serban, Rajjan and Raymund. "Proposed Heuristic Procedures to Preprocesses Character Pattern using Line Adjacency Graphs". Pattern recognition, vol. 29(6): 951-975, 1996.
[9] Loris Eynard, Hubert Emptoz, "Italic or Roman: Word Style Recognition Without A Priori Knowledge for Old Printed Documents", 10th International Conference on Document Analysis and Recognition, 2009
[10] Chaudhuri, B. B. and Garain, U. "Detection of Italic, Bold and All-Capital Words in Document Images", Proc. 14th Int. Conf. on Pattern Recognition (ICPR), Vol. 1, pp. 610- 612, 1998.

Abstract : This paper contains concept of data leakage, its causes of leakage and different techniques to protect and detect the data leakage. The value of the data is incredible, so it should not be leaked or altered. In the field of IT. huge database is being used. This database is shared with multiple people at a time. But during this sharing of the data, there are huge chances of data vulnerability, leakage or alteration. So, to prevent these problems, a data leakage detection system has been proposed. This paper includes brief idea about data leakage detection and a methodology to detect the data leakage persons.
Keywords–IT, watermarking guilty agent, explicit data, DLP (data leakage prevention).

[1] Technical Report TR-BGU-2409-2010 24 Sept. 2010 1 A Survey of Data Leakage Detection and Prevention Solutions P.P (1-5, 24-25) A. Shabtai, a. Gershman, M. Kopeetsky, y. Elovici Deutsche Telekom Laboratories at Ben-Gurion University, Israel.
[2] IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 3, MARCH 2011 Data Leakage Detection Panagiotis Papadimitriou, Member, IEEE, Hector Garcia-Molina, Member, IEEE P.P (2,4-5)
[3] Data Leakage: What You Need to Know by Faith M. Heikkila, Pivot Group Information Security Consultant. P.P (1-3)
[4] International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE II, JUNE 2011] [ISSN: 2231-4946] P.P (1, 4) Development of Data leakage Detection Using Data Allocation Strategies Rudragouda G Patil Dept of CSE, The Oxford College of Engg, Bangalore.
[5] Mr.V.Malsoru, Naresh Bollam/ International Journal of Engineering Research and Applications (IJERA) ISSN:2248-9622 www.ijera.com Vol. 1, Issue 3, pp.1088-1091 1088 | P a g e REVIEW ON DATA LEAKAGE DETECTION.
[6] Mr.V.Malsoru, Naresh Bollam/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 1, Issue 3, pp.1088-1091 1088 | P a g e REVIEW ON DATA LEAKAGE DETECTION.
[7] International Journal of Computer Applications in Engineering Sciences[VOL I, ISSUE II, JUNE 2011] [ISSN: 2231-4946] P.P (1, 4)Development of Data leakage Detection Using Data Allocation StrategiesRudragouda G Patil Dept of CSE, The Oxford College of Engg, Bangalore.patilrudrag@gmail.com
[8] A Model for Data Leakage Detection Panagiotis Papadimitriou 1, Hector Garcia-Molina 2 Stanford University 353 Serra Street, Stanford, CA 94305, USA P.P (1, 4-5) 1papadimitriou@stanford.edu
[9] Web-based Data Leakage Prevention Sachiko Yoshihama1, Takuya Mishina1, and Tsutomu Matsumoto2 1 IBM Research - Tokyo, Yamato, Kanagawa, Japan fsachikoy, tmishinag@jp.ibm.com, P.P (2,14) 2 Graduate School of Environment and Information Sciences, Yokohama National University, Yokohama, Kanagawa, Japan tsutomu@ynu.ac.jp
[10] Data Leakage: Affordable Data Leakage Risk Management by Joseph A. Rivela Senior Security Consultant P.P (4-6)

Abstract—In this paper a Binary Neural Network Learning (BNN-CLA)[1] is analyzed and implemented for solving multi class problem normally the classifier are construct by combining the outputs of several binary ones. The BNNC offers high degree of parallelism in hidden layer formation for all multiple classes to reduce the time for learning. The learning method is an iterative process to optimize the classifier parameters. In this approach, overlapping problem is tackled to enhance the performance of classifier by changing hyper-sphere radius. Exhaustive testing is carried out. Accuracies and number of neuron are evaluated and compare with BNNC[4]. The method have been tested on Fisher's well known Iris data data set and experimental result shown the classification ability improved by using FCLA algorithm. While comparing with BNNC[4] in most cases accuracies improved in BNNL because of elimination of samples which are lying in overlapping region of classes. Thus tacking overlapping issue improved performance of this classifier.
keyword: Semi-Supervised classification, BNN Geometrical Expansion, Hyper sphere, overlapped classes.

1. A. Tiwari, and N.S. Chaudhari, 􀳦Design of output codes for Fast Covering Learning using basic Decomposition Techniques,􀳦 Journal of Computer Science, (Science Publications, NY, USA), Vol. 2, No. 7, pp. 565-571 (July 2006).
2. D.Wang and N.S.Choudhari, "A novel training algorithm for Boolean neural networks based on multi level geometrical expansion," Neurocomputing 57C (2004) 455-461
3. Narendra S. Chaudhari and Aruna Tiwari ,"Binary Neural Network Classifier and it‟s Bound for the Number of Hidden Layer Neurons", ICARCV2010 978-1-4244-7815-6/10/$26.002010 IEEE
4. Di Wang, Narendra S. Chaudhari and Jagdish Chandra Patra, "Fast Constructive-Covering Approach for Neural Networks," School of Computer Engineering, Nanyang Technological University, Singapore,
5. J.H.Kim and S.K.Park, "The geometrical learning of binary neural neworks," IEEE Transaction. Neural Networks 6(1995) 237-247,
6. S. Gazula, and M. R. Kabuka, "Design of supervised classifiers using Boolean neural networks," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.17, No. 12, IEEE, USA, pp.1239-1246, Dec 1995.
7. Arvind Chandel, A. Tiwari, and N. S. Chaudhari, "Constructive Semi-Supervised Classification Algorithm and its Implement in Data Mining", Third International Conference on Pattern Recognition and Machine Intelligence (PReMI‟ 2009) (published in Lecture Notes in Computer Science, vol. no. 5909, ISBN 978-3-642-11163-1, SpringerVerlag Berlin Heidelberg) , IIT Delhi, India Dec. 2009.
8. Dan Zhang, Jingdong Wang, Fei Wang and Changshui Zhang, "Semi-Supervised Classification with Universum," State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084,
9. Di Wanga and Narendra. S. Chaudhari, "A constructive unsupervised learning algorithm for clustering binary patterns," in: Proceeding of international joint conference on Neural Networks 2004, vol.4, Budapest, Hungary, 2004, pp. 1381-1386,
10. Kishan Mehrotra, Chilukuri K. Mohan and Sanjay Ranka, 1997. Elements of Artificial Neural Networks : Cambridge, MA:MIT Press.
11. Thomas G. Dietterich, Ghulum. Bakiri,1995. Solving Multiclass Learning Problems via Error-Correcting Output Codes : Journal of Artificial Intelligence Research, Vol. 2 : 263-286.
12. Donald L. Gray and Anthony N. Michel, 1992. A training algorithm for binary feedforward neural networks. IEEE Trans : Neural Networks, Vol. 3,No. 2, IEEE, USA, pp :176-194

Abstract : Innovations are necessary to ride the inevitable tide of change. Most of enterprises are striving to reduce their computing cost through the means of virtualization. This demand of reducing the computing cost has led to the innovation of Cloud Computing. One fundamental aspect of this new computing is that data is being centralized or outsourced into the cloud. From the data owners perspective, including both individuals and IT enterprises, storing data remotely in a cloud in a flexible on-demand manner brings appealing benefits: relief of the burden of storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, personnel maintenance, and so on although the infrastructures under the cloud are much more powerful and reliable than personal computing devices, they still face a broad range of both internal and external threats to data integrity. Outsourcing data into the cloud is economically attractive for the cost and complexity of long-term large scale data storage, it does not offer any guarantee on data integrity and availability. We propose a distributed scheme to ensure users that their data are indeed stored appropriately and kept intact all the time in the cloud. We are using erasure correcting code in the file distribution preparation to provide redundancies. We are relaying on challenge response protocol along with pre-computed tokens to verify the storage correctness of user's data & to effectively locate the malfunctioning server when data corruption has been detected. Our scheme maintains the same level of storage correctness assurance even if users modify, delete or append their data files in the cloud.
Keywords - Cloud computing, Distributed data storage, Data security, Pervasive Computing, Virtualization.

[1] A. Juels, J. Burton, and S. Kaliski, "PORs: Proofs of Retrievability for Large Files," Proc. ACM CCS "07, Oct. 2007, pp. 584–97.
[2] G.Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, "Provable Data Possession at Untrusted Stores," Proc. ACM CCS "07, Oct. 2007, pp. 598–609.
[3] G. Ateniese, R. D. Pietro, L. V. Mancini, and G. Tsudik, "Scalable and Efficient Provable Data Possession," Proc. SecureComm "08, Sept. 2008.
[4] H. Shacham and B. Waters, "Compact Proofs of Retrievability," Proc. Asia-Crypt "08, LNCS, vol. 5350, Dec. 2008, pp. 90–107.
[5] K. D. Bowers, A. Juels, and A. Oprea, "Hail: A High-Availability and Integrity Layer for Cloud Storage," Proc. ACM CCS "09, Nov. 2009, pp. 187–98.
[6] C.Wang, Qian Wang, Kui Ren, Wenjing Lou, "Ensuring Data Storage Security in Cloud Computing," Proc. IWQoS "09, July 2009, pp. 1–9.
[7] Q. Wang, C.Wang, Wenjing Lou, Jin Li, "Enabling Public Verifiability and Data Dynamics for Storage Security in Cloud Computing," Proc. ESORICS "09, Sept. 2009, pp. 355–70.
[8] C. Erway, Alptekin, Charalampos Papamanthou, Roberto Tamassia, "Dynamic Provable Data Possession," Proc. ACM CCS "09, Nov. 2009, pp. 213–22.
[9] R. Curtmola, O. Khan, R. Burns, and G. Ateniese, "MR-PDP: Multiple-replica provable data possession," in Proc. of ICDCS‟08. IEEE Computer Society, 2008, pp. 411–420.
[10] T. Schwarz and E. L. Miller, "Store, forget, and check: Using algebraic signatures to check remotely administered storage," in Proc. of ICDCS‟06, 2006.......................