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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

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Volume 2 Issue 2 February - April 2015

Article

Data Scheduling and Mapreducing in Big Data

E.Ravi Kondal* , B.Mounika**

* Associate Professor, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

** B. Tech Student, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

Kondal, E. R., and Mounika, B. (2015). Data Scheduling and Mapreducing in Big Data. i-manager’s Journal on Cloud Computing, 2(2), 1-6. https://doi.org/10.26634/jcc.2.2.3445

Abstract

The volume of data usage is growing drastically day by day. Hence, it is not easy to maintain the data. In Big Data, huge amount of structured, semi-structured and unstructured data, produced daily by resources all over the world are stored in the computer. Mapreducing, a programming model, is used for implementing such large data sets. MapReduce program is used to collect data as per the request. To process the large volume of data, proper scheduling is used in order to achieve greater performance. Task scheduling plays a major role in Big Data cloud. Task scheduling contains a lot of rules to solve the problems of users and provides the quality of services to achieve the goal of that task to improve the resource utilization and turnaround time. Capacity and Delay Scheduling are used to improve the performance of the Big Data. This paper presents an overview of the Map-Reduce technique for shuffling and reducing the data and also the Capacity Scheduling and Delay Scheduling, for improving the reliability of the data

References

[1]. Michael Stonebreaker, Daniel Abadi, David J. DeWitt, Sam Madden, Erik Paulson, Andrew Pavlo and Alexander Rasin.,(2010). ”Map-Reduce complements DBMSs since databases are not designed for extract transform- load tasks, a Map-Reduce specialty”, ACM Digital Library, Vol.53, No.1, pp.64-71.

[2]. Radheshyam Nanduri, Niteshaheshwari, Reddy Raja, Vasudeva Varma, (2011). “Job Aware Scheduling Algorithm for Map-Reduce Framework”,3rd IEEE International Conference on Cloud Computing Technology and Science, 978-0-7695-4622-3/11, pp.724-729.

[3]. Dean, J. and Ghemawat, S.,(2010). “Map-Reduce: a flexible Data processing tool”, ACM Digital Library, Vol. 53, No.1, pp.72-77.

[4]. DeWitt & Stonebreaker (2008). “Map-Reduce: A major step Backwards”, page no.1/9, Online forum http://www.yjanboo.cn/?p=237

[5]. Dongjin Yoo, Kwang Mong Sim (2011). “A Comparative Review of job scheduling for Map-Reduce,” 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 353 – 358.

[6]. Zhao, Yaxiong , and Jie Wu (2013). "Dache: A data aware caching for big-data applications using the MapReduce framework", Tsinghua Science and Technology, Vol.19, No.1, pp. 39-50.

[7]. Dean, J. and Ghemawat, S.,(2010). “Map-Reduce: a flexible data processing tool”, ACM Digital Library, Vol. 53 No. 1, Pages 72-77.

[8]. E.Ravi Kondal.,( 2014). ”Efficient Cloud Seeding on Cloud Storage Using Virtualization”, International Journal of Scientific & Engineering Research, Vol.5, No.12, pp.1010-1019.

[9]. V. Krishna Reddy, B. Thirumala Rao, LSS Reddy, (2009). “Research issues in Cloud Computing”, Global Journal Computer Science & Technology, Vol. 9, No. 9, pp.70-76.

[10]. Jagmohan Chauhan, Dwight Makaroff and Winfried Grassmann.,(2012). “The Impact of Capacity Scheduler Configuration Settings on Map-Reduce Jobs”, 2012 Second International Conference on Cloud and Green Computing (CGC), pp. 667 – 674.

[11]. Hadoop's capacity scheduler:http://Hadoop. apache.org/cor e/ docs/current/ capacity/scheduler

[12]. B. Thirumala Rao, Dr. L. S. Reddy.,(2011). “Survey on Improved Scheduling in Hadoop Map-Reduce in Cloud Environments”,International Journal of Computer Applications, Vol.34, No.9, pp.29-33.

[13]. Harshawardhan S. Bhosle, Devendra P. Gadekar., (2014). “Big Data Processing Using Hadoop: Survey on Scheduling”, International Journal of Science and Research (IJSR),Vol.3, No. 10, pp.272-277.

[14]. Matei Zaharia, Dhruba Borthakur, Joy deep Sen Sarma, Khaled Elmeleegy, Scott Shenker, and Ion Stoica.,(2010). “Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling in EuroSys 10”, Proceedings of the 5th European Conference on Computer systems, pp.265–278.

[15]. Raja Manish Singh, Sanchita Paul, Abhishek Kumar., (2014).”Task Scheduling in Cloud Computing: Review”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol.5, No.6, pp. 7940-7944.

[16]. N.Saranya, Mr.T.Yoganandh.,(2014). “An Efficient Map Reduce Task Scheduling and Micro-Partitioning Mechanism for Optimizing Large Data Analysis”, International Journal on Engineering Technology and Sciences – IJETS, Vol.1, No.7, pp. 2349-3976.

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Article

Encroachment of Cloud Education for the Present Educational Institutions

D. R. Robert Joan*

Assistant Professor of Mathematics, Christian College of Education, Marthandam, Tamil Nadu, India.

Joan, D. R. R. (2015). Encroachment of Cloud Education for the Present Educational Institutions. i-manager’s Journal on Cloud Computing, 2(2), 7-13. https://doi.org/10.26634/jcc.2.2.3446

Abstract

In this article the author explains how cloud education can provide reasonable and high-value education services for contemporary students, teachers, parents and administrators. Also the benefits for students and faculties by cloud education in educational institutions are discussed as cloud education is very necessary for the information society institution. Technology will get integrated into every aspect of the institutions and thus cloud education will change the classrooms, games fields, gyms and school trips. Whether offsite or onsite, the school, teachers, students and support staff will all be connected. In the cloud education system, all the classrooms will be paperless and the world will become the classroom. E-learning will change teaching and learning process in the educational situations. Students can learn from anywhere and teachers can teach from anywhere. The cloud can also encourage independent learning. Teachers could adopt a flipped classroom approach and students can take ownership of their own learning. Teachers can put resources for students through online to use. These could be videos, documents, audio podcasts or interactive images. All of these resources can be accessed through a student's computer, smart-phone or tablet with an internet connection by Wifi, 3G or 4G.

References

[1]. ABS (2015). Cloud Computing and Education. Retrieved from https://www.advancedbusinesssolutions. com/blog/it-services/cloud-services/cloud-computingand education/

[2]. Bomfim, S. (2015). The Cloud: A Smart Move for Higher Education. Retrieved from http://www.ellucian.com/ Insights/The-Cloud--A-Smart-Move-for-Higher-Education/

[3]. Boyle, J. (2013). Learning in the cloud. Retrieved from http://www.edgazette.govt.nz/Articles/Article.aspx?Article Id=8853

[4]. Educause (2009). Things you should know about Cloud Computing. Retrieved from https://net.educause. edu/ir/library/pdf/EST0902.pdf

[5]. Educause (2010). Cloud Computing. Retrieved from http://www.educause.edu/library/cloud-computing.

[6]. Ercana, T. (2010). Effective use of cloud computing in educational institutions, Procedia Social and Behavioral Sciences, 2, 938–942, doi:10.1016/j.sbspro.2010.03.130

[7]. Fort, A. (2015). Use of Cloud-based Technologies in the Classroom. Retrieved from http://www.edutopia. org/ discussion / use - cloud - based - technologies - classroom

[8]. Google (2010). Google Apps. Retrieved from http://www.google.com/apps/

[9]. Karamete, A. (2015). Computer Education and Instructional Technology Teacher Trainees' Opinions about Cloud Computing Technology, Educational Research and Reviews, 10(14) 2043-2050.

[10]. Lahiri, M., & Moseley, J. L. (2013). Migrating Educational Data and Services to Cloud Computing: Exploring Benefits and Challenges. Educational Technology, 53(1) 20-30.

[11]. Lakshminarayanan, R., Kumar, B., & Raju, M. (2013). Cloud Computing Benefits for Educational Institutions. Retrieved from http://ar xiv.org/ftp/ar xiv/papers/ 1305/1305.2616.pdf

[12]. Staines, G. (2013). The Benefits of Cloud Computing in Education are Huge. Retrieved from http:// blogs.aspect.com/2013/06/27/the-benefits-of-cloudcomputing- in-education-are-huge/

[13]. Wang, M., Chen, Y., Khan, M. J. (2014). Mobile Cloud Learning for Higher Education: A Case Study of Moodle in the Cloud, International Review of Research in Open and Distance Learning, Vol. 15(2), pp. 254-267.

[14]. Schneckenberg, D. (2014). Easy, Collaborative and Engaging--The Use of Cloud Computing in the Design of Management Classrooms. Educational Research, Vol. 56(4), pp. 412-435.

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Research Paper

Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform

D. Kesavaraja* , A.Shenbagavalli**

* Assistant Professor, Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India.

** Professor & Head, Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India.

Kesavaraja, D., and Shenbagavalli, A. (2015). Scalable Video Transcoding with Hadoop Map Reduce in Openstack Juno Platform. i-manager’s Journal on Cloud Computing, 2(2), 14-21. https://doi.org/10.26634/jcc.2.2.3447

Abstract

Cloud computing and big data are changing today’s modern on demand video service.This paper describes how to increase the speed of video transcoding in an open stack private cloud environment using Hadoop Map Reduce. In this paper, OpenStack Juno is used to build the private cloud infrastructure as a service having map code executing on the node, where the video transcoding resides, to significantly reduce this problem. This practice, called “video locality”, is one of the key advantages of Hadoop MapReduce. This scheme describes the deep relationship of a Hadoop Map Reduce algorithm and video transcoding in the experiment. As a result of Map Reduce video transcoding experiment in openstack Juno, outstanding performance of the physical server was observed when running on the virtual machine in the private cloud based on the metrics, in terms of Time Complexity and Quality Check using PSNR (Peak Signal-to-Noise Ratio).

References

[1]. Steven C. Markey (2012). "Deploy an OpenStack private cloud to a Hadoop MapReduce environment”, IBM Developer Works.

[2]. [Online] Available :https://www.mirantis.com/blog/ improving-data-processing-per formance-hadoopdata- locality/

[3]. Andrew Lazarev (2014). "Performance of Hadoop on OpenStack", Mirantis [Onine] Available: https:// www.openstack.org/assets/presentation-media/ Performance-of-Hadoop-on-OpenStack.pdf

[4]. G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, (2007). “Dynamo: Amazon's highly available key-value store,” in SOSP, pp. 205-220.

[5]. D. Beaver, S. Kumar, H. C. Li, J. Sobel, P. Vajgel, (2010). “Finding a needle in haystack: Facebook's photo storage.” in OSDI

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[7]. J. Dean and S. Ghemawat, (2004). “Mapreduce: Simplified data processing on large clusters,” in OSD

[8].[Online]Available“Apache HDFS,” http:// hadoop.apache.org/docs/r1.2.1/hdfs design. html.

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[12]. M. Mihailescu, G. Soundararajan, and C. Amza, (2013).“Mixapart: decoupled analytics for shared storage systems.” in FAST

[13]. S. Sehrish, G. Mackey, J. Wang, and J. Bent, (2010). “Mrap: a novel Mapreduce based framework to support HPC analytics applications with access patterns,” in HPDC

[14]. Lukas Rupprecht, Rui Zhang , Dean Hildebrand,“Big Data Analytics on Object Stores: A Performance Study” , The International Conference for High Performance Computing, Networking, Storage and Analysis.

[15]. M. Armbrust, A. Fox, R. Griggith, (2009). "Above the cloud: A Berkeley View of Cloud Computing," Technical Report No.UCB/EECS-2009-28, EECS Department, University of California at Berkeley, USA

[16]. Yunhee Kang, Geoffrey C. Fox, (2011). “Performance Evaluation of MapReduce Applications on Cloud Computing Environment, FutureGrid”, Grid and Distributed Computing: Vol. 261, pp. 77-86.

[17]. J. Dean and S. Ghemawat, (2010). "MapReduce: A Flexible Data Processing Tool," Communications of the ACM, Vol. 53, pp. 72-77

[18]. J. Ekanayake, (2008). "MapReduce for Data Intensive Scientific Analyses," the Fourth IEEE International Conference on eScience, pp. 277-284.

[19]. [Online] Available Hadoop. http://hadoop. apache.org/

[20]. [Online] Available OpenStack http://www.openstack.org/

[21]. Yunhee Kang, Kyung-Woo Kang (2013).“An Empirical Study of Hadoop Application running on Private Cloud Environment”, Advanced Science and Technology Letters, Vol. 35(Cloud and Super Computing), pp.70-73.

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Research Paper

Auditing the Shared Data in Cloud through Privacy Preserving Mechanisms

V.Ramya Sai* , Lokanadham Naidu Vadlamudi, Srinivasulu Asadi*

Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh

* Ph.D Scholar, Jawaharlal Nehru Technological University, Anantapur, India.

Sai, V. R., Naidu, V. L., and Srinivasulu, A. (2015). Auditing the Shared Data in Cloud through Privacy Preserving Mechanisms. i-manager’s Journal on Cloud Computing, 2(2), 22-30. https://doi.org/10.26634/jcc.2.2.3448

Abstract

With cloud storage services, it is routine that data is not only stored in the cloud, but also shared among group of users. However, public auditing for such distributed data at the same time by preserving identity privacy remains a challenge. This paper proposes a privacy preserving public auditing technique for shared data called ‘Oruta’ which supports auditing, data privacy and identity privacy. This scheme is mainly focused on ring signatures to calculate the information needed for verification to audit the integrity of shared data. By using this auditing, the signer identity on each block is kept unconditionally private from a Third Party Auditor (TPA). This paper also aims to implement traceability, i.e.., the group manager or the original user will know who are editing the data in some special situations based on verification of Metadata. By doing this, the privacy will be preserved and the group manager or the original user can trace the signer, so the original user can have complete control over the data and the signers who are accessing the data.

References

[1]. Boyang Wang, (2014). “Oruta: Privacy Preserving Public Auditing for Shared Data in the Cloud”, IEEE Transactions on Cloud Computing, Vol. 2, No. 1, pp. 43- 56.

[2]. C. Wang, Q. Wang, K. Ren, and W. Lou, (2010). “Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing,” Proc. IEEE INFOCOM, pp. 525-533.

[3]. Wang J, Zhao Y et al. (2009). “Providing Privacy Preser ving in cloud computing ”, International Conference on Test and Measurement, Vol. 2, pp. 213–216.

[4]. Wang B, Li B et al. (2012). “Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud”, IEEE Fifth International Conference on Cloud Computing, pp. 295–302.

[5]. Wang C, Chow S S M et al. (2013). “Privacy-Preserving Public Auditing for Secure Cloud Storage, IEEE Transactions on Computers, Vol. 62, No. 2, pp. 362–375.

[6]. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, (2007). “Provable Data Possession at Untrusted Stores,” Proc. 14th ACM Conf. Computer and Comm. Security (CCS '07), pp. 598-610.

[7]. B. Wang, M. Li, S.S. Chow, and H. Li, (2013). “Computing Encrypted Cloud Data Efficiently under Multiple Keys,” Proc. IEEE Conf. Comm. and Network Security (CNS '13), pp. 90-99.

[8]. H. Shacham and B. Waters, (2008). “Compact Proofs of Retrievability”, Proc. 14th Int'l Conf. Theory and Application of Cryptology and Information Security: Advances in Cryptology (ASIACRYPT '08), pp. 90-107.

[9]. C. Erway, A. Kupcu, C. Papamanthou, and R. Tamassia, (2009). “Dynamic Provable Data Possession,” Proc. 16th ACM Conf. Computer and Comm. Security (CCS'09), pp. 213-222.

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Research Paper

A Scalable and Cost-Effective Data Anonymization over Big Data using MapReduce on Cloud

Shalin Elizabeth. S* , S.Sarju**

* M.Tech Student, St Josephs College of Engineering and Technology, Kerala, India.

** Assistant Professor, Department of Computer Science and Engineering, St.Josephs College of Engineering and Technology, Kerala, India.

Elizabeth. S. S., and Sarju, S. (2015). A Scalable and Cost-Effective Data Anonymization over Big Data using Mapreduce on Cloud. i-manager’s Journal on Cloud Computing, 2(2), 31-39. https://doi.org/10.26634/jcc.2.2.3449

Abstract

In big data applications, data privacy is one of the most important issues on processing large-scale privacy-sensitive data sets, which requires computation resources provisioned by public cloud services.It refers to the commercial "aggregation, mining, and analysis" of very large, complex and unstructured datasets. Due to its large size, discovering knowledge or obtaining pattern from big data within an elapsed time is a complicated task. The cloud and the advances in big data mining and analytics have expanded the scope of information available to businesses, government, and individuals. The internet users also share their private data like health records and financial transaction records for mining or data analysis purpose. For which, data anonymization is used for hiding identity or sensitive intelligence. This paper investigates the problem of big data anonymization for privacy preservation from the perspectives of scalability and cost-effectiveness. Anonymizing large scale data within a short span of time is a challenging task. To overcome that, Enhanced Top –Down Specialization approach (ETDS) can be developed which is an enhancement of Two –Phase Top Down Specialization approach (TPTDS). Accordingly, a scalable and cost-effective privacy preserving framework is developed to provide a holistic conceptual foundation for privacy preservation over big data which enable users to accomplish the full potential of the high scalability, elasticity, and cost-effectiveness of the cloud. The multidimensional anonymization of MapReducing framework will increase the efficiency of the big data processing system.

References

[1]. Xuyun Zhang, Laurence T. Yang, Chang Liu and Jinjun Chen,(2014). “A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization using MapReduce on Cloud”, IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 25, No. 2, pp. 263-373, ISSN: 1045-9219. (A*, IF: 1.796)

[2]. Wanchun Dou, Xuyun Zhang, Jianxun Liu and Jinjun Chen, (2013). “HireSome-II: Towards Privacy-Aware Cross- Cloud Service Composition for Big Data Applications”, IEEE Transactions on Parallel and Distributed Systems (TPDS), in press, (A*, IF: 1.796), No. 1, pp. 1.

[3]. S.Chaudhuri, (2012). “What Next? A Half-Dozen Data Management Research Goals for Big Data and the Cloud,” Proc. 31st Symp. Principles of Database Systems (PODS '12), pp. 1-4.

[4]. M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A.Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M.Zaharia,(2010). “A View of Cloud Computing,” Comm. ACM, Vol. 53, No. 4, pp. 50-58.

[5]. L. Wang, J. Zhan, W. Shi, and Y. Liang, (2012). “In Cloud, Can ScientificCommunities Benefit from the Economies of Scale?,” IEEE Trans.Parallel and Distributed Systems, Vol. 23, No. 2, pp.296-303.

[6]. H. Takabi, J.B.D. Joshi, and G. Ahn, “Security and Privacy Challenges in Cloud Computing Environments,” IEEE Security and Privacy, Vol. 8, No. 6, pp. 24-31.

[7]. D. Zissis and D. Lekkas, (2011). “Addressing Cloud Computing Security Issues,” Future Generation Computer Systems, Vol. 28, No. 3, pp. 583-592.

[8]. X. Zhang, C. Liu, S. Nepal, S. Pandey, and J. Chen, (2012). “A Privacy Leakage Upper-Bound Constraint Based Approach for Cost-Effective Privacy Preserving of Intermediate Data Sets in Cloud,” IEEE Trans. Parallel and Distributed Systems, Vol. 24, No. 6, pp. 1192-1202.

[9]. L. Hsiao-Ying and W.G. Tzeng, (2012). “A Secure Erasure Code-BasedCloud Storage System with Secure Data Forwarding,” IEEE Trans.Parallel and Distributed Systems, Vol. 23, No. 6, pp. 995-1003.

[10]. N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, (2011). “Privacy Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data,” Proc.IEEE INFOCOM, pp. 829- 837.

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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

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Volume 2 Issue 2 February - April 2015

Data Scheduling and Mapreducing in Big Data

E.Ravi Kondal* , B.Mounika**

* Associate Professor, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India. ** B. Tech Student, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

Kondal, E. R., and Mounika, B. (2015). Data Scheduling and Mapreducing in Big Data. i-manager’s Journal on Cloud Computing, 2(2), 1-6. https://doi.org/10.26634/jcc.2.2.3445

Abstract

References

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Encroachment of Cloud Education for the Present Educational Institutions

D. R. Robert Joan*

Assistant Professor of Mathematics, Christian College of Education, Marthandam, Tamil Nadu, India.

Joan, D. R. R. (2015). Encroachment of Cloud Education for the Present Educational Institutions. i-manager’s Journal on Cloud Computing, 2(2), 7-13. https://doi.org/10.26634/jcc.2.2.3446

Abstract

References

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Scalable Video Transcoding with Hadoop MapReduce in Openstack Juno Platform

D. Kesavaraja* , A.Shenbagavalli**

* Assistant Professor, Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India. ** Professor & Head, Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India.

Kesavaraja, D., and Shenbagavalli, A. (2015). Scalable Video Transcoding with Hadoop Map Reduce in Openstack Juno Platform. i-manager’s Journal on Cloud Computing, 2(2), 14-21. https://doi.org/10.26634/jcc.2.2.3447

Abstract

References

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Auditing the Shared Data in Cloud through Privacy Preserving Mechanisms

V.Ramya Sai* , Lokanadham Naidu Vadlamudi**, Srinivasulu Asadi***

** Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh *** Ph.D Scholar, Jawaharlal Nehru Technological University, Anantapur, India.

Sai, V. R., Naidu, V. L., and Srinivasulu, A. (2015). Auditing the Shared Data in Cloud through Privacy Preserving Mechanisms. i-manager’s Journal on Cloud Computing, 2(2), 22-30. https://doi.org/10.26634/jcc.2.2.3448

Abstract

References

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A Scalable and Cost-Effective Data Anonymization over Big Data using MapReduce on Cloud

Shalin Elizabeth. S* , S.Sarju**

* M.Tech Student, St Josephs College of Engineering and Technology, Kerala, India. ** Assistant Professor, Department of Computer Science and Engineering, St.Josephs College of Engineering and Technology, Kerala, India.

Elizabeth. S. S., and Sarju, S. (2015). A Scalable and Cost-Effective Data Anonymization over Big Data using Mapreduce on Cloud. i-manager’s Journal on Cloud Computing, 2(2), 31-39. https://doi.org/10.26634/jcc.2.2.3449

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* Associate Professor, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

** B. Tech Student, Department of Computer Science and Engineering, Noble College of Engineering & Technology for Women, Hyderabad, India.

* Assistant Professor, Department of Computer Science and Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, India.

** Professor & Head, Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India.

V.Ramya Sai* , Lokanadham Naidu Vadlamudi, Srinivasulu Asadi*

Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh

* Ph.D Scholar, Jawaharlal Nehru Technological University, Anantapur, India.

* M.Tech Student, St Josephs College of Engineering and Technology, Kerala, India.

** Assistant Professor, Department of Computer Science and Engineering, St.Josephs College of Engineering and Technology, Kerala, India.