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Volume 2 Issue 1 November - January 2015

Article

Survey on Emerging Technologies for Secure Computations of Big Data

Madhusudhana Nooka Reddy* , C. Naga Raju**

* Associate Professor, Department of Computer Science and Engineering,Syamala Devi Institute of Technology for Women, Andhra Pradesh, India.

** Associate Professor and Head, Department of Computer Science and Engineering, Yogi Vemana University, Andhra Pradesh, India.

Reddy, N. N., and Raju, C. N. (2015). Survey on Emerging Technologies for Secure Computations of Big Data. i-manager’s Journal on Cloud Computing, 2(1), pp. 1-6. https://doi.org/10.26634/jcc.2.1.3300

Abstract

Big data is vast amount of data characterized by volume, velocity and variety. Mining such data can provide comprehensive business intelligence. However, conventional environments are not sufficient to handle and mine such data. Distributed programming frameworks like Hadoop are used to process big data. Such frameworks use a new programming paradigm known as Map Reduce. The challenging issue in big data mining is security implications. This paper explores the merits and demerits of the distributed data mining frameworks such as Hadoop, Haloop, Sailfish and AROM and describes the techniques on how the distributed frameworks for managing and mining big data can help enterprises to make expert decisions. Moreover there is a need for secure computations in distributed programming frameworks. This paper provides useful insights on big data, big data mining and the need for secure computations for processing big data.

References

[1]. Takahashichieko, sera naohiko, Tsukumotokenji, osakihirotatsu (2012). “osshadoop use in big data processing”, Nec Technical Journal. pp.1-5.

[2]. Dibyendubhattacharya (2013). “Analytics on Big Fast data using real time stream data processing architecture”. Emc Proven Professional Knowledge Sharing, pp.1-34.

[3]. Liraneinav and Jonathanlevin (2013). “The Data Revolution and Economic Analysis”, Prepared for the NBER Innovation Policy and the Economy Conference. pp. 1-29.

[4]. An Oracle White Paper (2013). Oracle: Big Data for the Enterprise. USA: Oracle , pp.1-16.

[5]. Australian Government. (2013). Big Data Strategy – Issues Paper. Department of Finance and Deregulation, pp.1-12.

[6]. Leading researchers across the United States. (n.d). Challenges and Opportunities with Big Data. Leading Researchers. pp.1-17.

[7]. SAS. (2012). Big data Lessons from the leaders. Economist Intelligence Unit Limited. pp. 1-30.

[8]. Dawei Jiang, Gang Chen, Beng Chin Ooi, Kian Lee Tan, Sai Wu (2010). “Epic: An Extensible and Scalable System for Processing Big Data”, pp.1-12.

[9]. Michael Cooper & Peter Mell (2013). “Tackling Big Data”, National Institute of Standards and Technology, pp.1-40.

[10]. Michael Schroeck, Rebecca Shockley, Dr. Janet Smart, Professor Dolores Romero-Morales and Professor Peter Tufano (2012). “Analytics: The real-world use of big data”, IBM Global Business Services, pp.1-20.

[11]. Intel. (2013). Planning Guide Getting Started With Big Data. Intel IT Center, pp.1-24.

[12]. Mike Ferguson (2012). “Architecting A Big Data Platform for Analytics”, Intelligent Business Strategies, pp.1-36.

[13]. Intel. (n.d). Transforming Big Data into Big Value. Inte Distribution for Apache Hadoop, pp.1-10.

[14]. Jeffrey Dean and Sanjay Ghemawat. (2004). “MapReduce: Simplified Data Processing on Large Clusters”, pp. 1-13.

[15]. McKinsey (2011). “Big data: The next frontier for innovation, competition, and productivity”, MGI, pp. 1- 156.

[16]. Economics Intelligent Unit. (2011). Big data Harnessing a game-changing asset. Economics Intelligent Unit, pp.1-32.

[17]. IDF13. (n.d). Beyond Hadoop Map Reduce: Processing Big Data. Intel, pp.1-56.

[18]. Micron. (n.d). SSDs for Big Data – Fast Processing Requires High-Performance Storage. Micron Technology Inc, pp. 1-4.

[19]. Chris Yiu. (2012). “The Big Data Opportunity”, Policy Exchange, pp.1-36.

[20]. Dr. Nathan EagleBs (2010). “Big Data, Big Impact: New Possibilities for International Development”, The World Economic Forum, pp.1-10.

[21]. BogdanGhit¸ AlexandruIosup and Dick Epema (2005). “Towards an Optimized Big Data Processing System”, IEEE, pp.1-4.

[22]. ToyotaroSuzumura (2012), “Big Data Processing in Large-Scale Network Analysis and Billion-Scale Social Simulation”, IBM Research, pp.1-2.

[23]. ChangqingJi, YuLi, WenmingQiu , UchechukwuAwada, Keqiu Li (2012). “Big Data Processing in Cloud Computing Environments”, International Symposium on Pervasive Systems, pp. 1-7.

[24]. Yingyi Bu, Bill Howe, Magdalena Balazinska and Michael D. Ernst (2010). “HaLoop: Efficient Iterative Data Processing on Large Clusters”, IEEE, pp.1-12.

[25]. XIAO DAWEI, AO LEI (2013). “Exploration on Big Data Oriented Data Analyzing and Processing Technology”, IJCSI International Journal of Computer Science, pp.1-6.

[26]. Ling LIU (2012). “Computing Infrastructure for Big Data Processing”, USA: IEEE, pp.1-9.

[27]. Kevin McGowan (2013). “Big data: The next frontier for innovation, competition, and productivity”, USA: SAS Solutions on Demond, pp.1-16.

[28]. SriramRao, Raghu Ramakrishnan and Adam Silberstein (2012). “Sailfish: A Framework For Large Scale Data Processing”, USA: Microsoft, pp.1-14.

[29]. Nam-Luc Tran and SabriSkhiri and Arthur Lesuisse and Esteban Zim´anyi (2012). “AROM: Processing Big Data With Data Flow Graphs and Functional Programming”, Belgium: Amazon, pp.1-8.

[30]. MaheswaranSathiamoorthy, MegasthenisAsteris and DimitrisPapailiopoulos (2013). “XORing Elephants: Novel Erasure Codes for Big Data”, Proceedings of the VLDB Endowment, pp.1-12.

[31]. Steps IT managers can take to move Forward with apache Hadoop Software. (2013). Planning Giuide Getting Started With Big Data, pp.1-24.

[32]. Sriram Rao, Raghu Ramakrishnan and Adam Silberstein (2012). “A Framework For Large Scale Data Processing”, USA: Microsoft, pp.1-14.

[33]. I. Roy, S. T. V. Setty, A. Kilzer, V. Shmatikov and E. Witchel, (2010). “Airavat: security and privacy for MapReduce” in USENIX Conference on Networked systems design and implementation, pp 20.

[34]. B. Sullivan, (2011). “NoSQL, But Even Less Security”, http://blogs.adobe.com/asset/files/2011/04/NoSQL-But- Even-Less-Security.pdf.

[35]. D. Boyd, and K. Crawford (2012). “Criticial Questions for Big Data,” Information, Communication & Society, pp. 662-675.

[36]. Aciiçmez, Onur, ÇetinKoç, and Jean-Pierre Seifert (2006). “Predicting secret keys via branch prediction”, Topics in Cryptology–CT-RSA, pp. 225-242.

[37]. C. Percival, (2005). “Cache missing for fun and profit”, BSDCan.

[38]. T. Ptacek and T. Newsham (1998). “Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection”, Tech. Report.

[39]. M. Barreno, B. Nelson, R. Sears, A. Joseph, J.D. Tygar, (2006). “Can Machine Learning be Secure?”. Proc. of the 2006 ACM Symposium on Information, Computer, and Communications Security, ASIACCS 2006, pp. 16-25.

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Article

Study of Cloud Based Mobile Learning Approaches

S.Vasundara* , D. Venkatesh, D. Raghavaraju*

* Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapuramu, India.

Associate Professor, Department of Computer Science and Engineering and Information Technology, GATES Institute of technology, Gooty, India.

* Assistant Professor, Department of Computer Science and Engineering, Sri Venkateswara Institute of Technology, Anantapuramu, India.

Vasundara, S., Venkatesh, D., and Raghavaraju, D. (2015). Study of Cloud Based Mobile Learning Approaches. i-manager’s Journal on Cloud Computing, 2(1), 7-13. https://doi.org/10.26634/jcc.2.1.3301

Abstract

Mobile cloud learning is a blend of portable learning and distributed computing, which is generally a new idea that holds significant guarantee for future improvement and conveyance in the instruction areas. Distributed computing helps versatile learning overcome hindrances identified with portable registering. Versatile learning (m-learning) has been acknowledged as a productive method for learning. With the expanding number of clients, administrations, training substance and assets, notwithstanding, how to send m-learning gets to be hazardous. As a promising innovation to beat the issues in m-learning, Cloud processing gives solid, redid and QoS ensured element registering situations for end-clients. The main aim of this paper is to provide different systems that provide mobile learning in cloud computing. In this study, characteristics and benefits of mobile learning are discussed.

References

[1]. Huang, S., & Yin, H (2012). “A New Mobile Learning Platform Based on Mobile Cloud Computing”, Advances in Future Computer and Control Systems Advances in Intelligent and Soft Computing, Vol. 159, pp. 393-398.

[2]. Elias, T (2011). “Universal instructional design principles for mobile learning”. Irrodl. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/965/16 75.

[3]. Wang, M., Chen, Y., & Khan, M. (2004). “Mobile Cloud Learning for Higher Education: A Case Study of Moodle in the Cloud”. The International Review of Research in Open and Distributed Learning, Vol. 15, No. 2, pp. 254-267.

[4]. Weiss,Cr (2012). State of the LMS. Retrieved from http://elearninfo247.com/2012/02/16/state-of-the-lms- 2012/.

[5]. Barac, D., Radenkovic, M., & Jovanic, B. (2014). “Mobile Learning Services on Cloud”. Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education, pp. 147-172.

[6]. Benjamin, H., & W. P. Ng, J (2011). “Education beyond the cloud: Anytime-anywhere learning in a smart campus environment ”, Internet Technology and Secured Transactions (ICITST), pp. 718-723.

[7]. Ullrich, C., Shen, R., Tong, R., & Tan , X. (2010). “A Mobile Live Video Learning Systemfor Large-Scale Learning—System Design and Evaluation”, IEEE Transactions on Learning Technologies, Vol. 3,No. 1, pp. 6-17.

[8]. Koyamat, A., Sasakit , A., Barolli, L., & Chengt, Z. (2001). “An Agent Based Education Systemfor Cellular Phone Database and expert systems application”, IEEE th 12 International conference, pp. 198-202.

[9]. BAI, X. (2010). “Affordance of Ubiquitous learning through cloud computing ”, IEEE International Conference on Frontier of Computer Science and Technology, pp. 78-82.

[10]. Chen, J., & LU, H. (2010). “The research and design of intelligence wireless Mobile Learning platform based on 3G”, IEEE International Conference on e-Business and Information System Security, pp. 1-4.

[11]. LI, J. (2010). “Study on the Development of Mobile Learning Promoted by Cloud Computing”, IEEE International Conference on Information Engineering and Computer Science , pp. 1-4.

[12]. Chen, X., Liu, J., Han, J., & Xu, H. (2010). “Primary Exploration of Mobile Learning Mode under a Cloud Computing Environment”, IEEE International Conference on E-Health Networking, Digital Ecosystems and Technologies (EDT), Vol. 2, pp. 484-487.

[13]. Gao, H., & Zai, Y. (2010). “System Design of Cloud Computing Based on Mobile Learning”, IEEE International Conference on Knowledge Acquisition and Modeling, pp. 239-242.

[14]. Zhang, L., Xue, Q., Liu, H., & Yuan, J. (2010). “Research on 3G mobile learning based on cloud service”, IEEE International Conference on E-Product EService and Entertainment, pp. 1-4. 2010.

[15]. Coasta, Ojala, & Korhonen,.(2008). “Mobile Lecture Interaction: Making Technology and Learning Click”. IADIS International Conference Mobile Learning, pp. 119-124.

[16]. Sharma, S., & Kitchens, F. (2004). “Web-Services Arcticture for M-Learning”, Electronic Journal on ELearning, Vol. 2, No. 1, pp. 203-216.

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Article

Research Issues in Enterprise Cloud Computing

S. Anandamurugan*

Assistant Professor (SLG), Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India

Anandamurugan, S. (2015). Research Issues in Enterprise Cloud Computing. i-manager’s Journal on Cloud Computing, 2(1), 14-23. https://doi.org/10.26634/jcc.2.1.3302

Abstract

Every technology has a value in it. To speak technically apart from theoretical and research, every technology must be implemented in real time to the human kind and solve all their issues. For every industry, profit is the major goal or a part of the whole business strategy itself. Thus every technology must be industry oriented and supported rather than a research topic every time.[1] Industrial oriented term of cloud computing is called as enterprise cloud computing. The word enterprise cloud computing refers to the business related operations and the usage of cloud computing technology for all their work. The close alignment of business related corporate IT sector and cloud computing has given a new dimension to the term enterprise cloud computing. Today cloud computing has changed the whole work culture in the corporate sector. Things have advanced very fast and have become more reliable. Gone are all the traditional and simple ways in handling all problems and solutions to them. Business related operations and solutions are well handled and managed using advanced remote cloud infrastructure and its unique capabilities. The objective of this paper is to give the overview of research issues in cloud computing

References

[1]. Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M. (2009). “Above the Clouds: A Berkeley View of Cloud Computing”, Technical Report. University of California at Berkeley. pp. 1-23.

[2]. Assuncao, M., Costanzo, A. And Buyya, R. (2009). “Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters”, In HPDC '09: Proceedings of the 18th ACM international symposium on High performance distributed computing, Munich, Germany, June 2009, pp. 141-150.

[3]. Barroso, L. A. and Hölzle, U. (2009). “The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines”, Morgan & Claypool Publishers, pp. 108.

[4]. Birman, K., Chockler, G., and van Renesse, R. (2009). “Toward a cloud computing research agenda”, SIGACT News, Vol. 40, No. 2, pp. 68-80.

[5]. Boxer, P., Carney, D.J., Garcia, S., Brownsword, L., Anderson, B., Kirwan, P., Smith, D.B. and Morley, J. (2008). “SoS Navigator 2.0: A Context-Based Approach to System-of-Systems Challenges”, Technical Report. Carnegie Mellon University Software Engineering Instiutute (SEI). CMU/SEI-2008-TN-001, pp. 1-48.

[6]. Buyya, R., Stockinger, H., Giddy, J. and Abramson, D. (2001). “Economic Models for Management of Resources in Grid Computing”, In SPIE International Symposium on The Convergence of Information Technologies and Communications (ITCom 2001), Denver, Colorado, USA, August 2001.

[7]. Carr, N. (2008). “The Big Switch: Rewiring the World”, from Edison to Google. W.W. Norton & Co, pp. 278.

[8]. Catteddu, D. and Hogben, G. (2009). “Cloud Computing: benefits, risks and recommendations for information security”, Technical Report. European Network and Information Security Agency, pp. 1-125.

[9]. Chappell, D. (2009). “Introducing the Azure Services Platform”, Whitepaper. David Chappell & Associates, pp. 1-31.

[10]. Clearwater, S. and Huberman, B. A. (2005). “Swing Options: a Mechanism for Pricing IT Peak Demand”, In 11th International Conference on Computing in Economics, Washington D.C., USA, June 2005.

[11]. Cloud Security Alliance. (2009). “Security Guidance for Critical Areas of Focus in Cloud Computing”, pp. 1-76.

[12]. Cortada, J.W. (2000). Before the computer: IBM, NCR, Burroughs, and Remington Rand and the industry they created 1865-1956. Princeton University Press. New Jersey.

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Research Paper

Multi Layer Encryption using Access Control in PublicClouds

K.Nethaji Sundar Sukumar* , L. Venkateswara Reddy**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India.

** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India.

Sukumar, K. N. S. S., and Reddy, L. V. (2015). Multi Layer Encryption using Access Control in Public Clouds. i-manager’s Journal on Cloud Computing, 2(1), 24-31. https://doi.org/10.26634/jcc.2.1.3303

Abstract

Nowadays privacy preserving is the challenging issue of public clouds. The data of fine-grained access control are enforced on confidential data hosted into the public clouds of storage. Single layer encryption (SLE) approach is to encrypt the data into public clouds by using encryption algorithm, and two layer encryption (TLE) approach is to encrypt the data before uploading into the clouds on storage. These two approaches are facing the problem of computational cost and communications process of data owner and public clouds. So, these problems are reduced by using new approach of multi layer encryption approach. In this approach, multiple keys are provided for end users and accessing the data from public clouds. So, the authors are using an algorithm known as Attribute Based-Group Key Management (AB-GKM).

References

[1]. Mohamed Nabeel and Elisa Bertino (2014), “Privacy Preserving Delegated Access Control in Public Clouds", IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 9, pp. 2268 – 2280.

[2]. V. Goyal, O. Pandey, A. Sahai, and B. Waters (2006), “Attribute-Based Encryption for Fine-Grained Access Control of Encrypted Data,” Proc. 13th ACM Conf. Computer and Comm. Security (CCS '06), pp. 89-98.

[3]. E. Bertino and E. Ferrari (2002), “Secure and Selective Dissemination of XML Documents,” ACM Trans. Information and System Security, Vol. 5, No. 3, pp. 290- 321.

[4]. M. Nabeel and E. Bertino (2012), “Privacy Preserving Delegated Access Control in the Storage as a Service Model,” Proc. IEEE Int'l Conf. Information Reuse and Integration (IRI), pp. 645 – 652.

[5]. M. Nabeel and E. Bertino (2014), “Attribute Based Group Key Management,” to appear in Trans. Data Privacy, Vol.7, No. 3,pp.309-336.

[6]. X. Liang, Z. Cao, H. Lin, and J. Shao (2009), “Attribute Based Proxy Re-Encryption with Delegating Capabilities,” Proc. Fourth Int'l Symp.Information, Computer, and Comm. Security (ASIACCS '09), pp. 276-286.

[7]. G. Miklau and D. Suciu (2003), “Controlling Access to Published Data Using Cryptography,” Proc. 29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 898-909.

[8]. N. Shang, M. Nabeel, F. Paci, and E. Bertino (2010), “A Privacy-Preserving Approach to Policy-Based Content Dissemination,” Proc. IEEE 26th Int'l Conf. Data Eng. (ICDE '10), pp. 944 – 955.

[9]. M. Nabeel, N. Shang, and E. Bertino (2013), “Privacy Preserving Policy Based Content Sharing in Public Clouds,” IEEE Trans. Knowledge and Data Eng., Vol. 25, No. 11, pp. 2602-2614.

[10]. M. Nabeel and E. Bertino (2011), “Towards Attribute Based Group Key Management,” Proc. 18th ACM Conf. Computer and Comm. Security, pp. 821-824.

[11]. M.Nabeel, E. Bertino, M. Kantarcioglu, and B.M. Thuraisingham (2011), “Towards Privacy Preserving Access Control in the Cloud,” Proc. Seventh Int'l Conf. Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom '11), pp. 172-180.

[12]. M. Pirretti, P. Traynor, P. McDaniel, and B. Waters (2006), “Secure Attribute-Based Systems,” Proc. 13th ACM Conf. Computer and Comm. Security (CCS '06), pp. 99- 112.

[13]. J. Li and N. Li (2006), “OACerts: Oblivious Attribute Certificates,” IEEE Trans. Dependable and Secure Computing, Vol. 3, No. 4, pp. 340-352.

[14]. T. Pedersen (1992), “Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing,” Proc. 11th Ann. Int'l Cryptology Conf. Advances in Cryptology (CRYPTO '91), pp. 129-140.

[15]. A. Sahai and B. Waters (2005), “Fuzzy Identity-Based Encryption,” Proc. 24th Ann. Int'l Conf. Theory and Applications of Cryptographic Techniques(Eurocrypt '05), pp. 457-473.

[16]. J. Bethencourt, A. Sahai, and B. Waters (2007), “Ciphertext-Policy Attribute-Based Encryption,” Proc. IEEE Symp. Security and Privacy (SP'07), pp. 321-334.

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Research Paper

Cloud Video Server (CVS): Secure Video Storage on Cloud using Heterogeneous Slaves

D. Kesavaraja* , A.Shenbagavalli**

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

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

Kesavaraja, D., and Shenbagavalli, A. (2015). Cloud Video Server (CVS): Secure Video Storage on Cloud using Heterogeneous Slaves. i-manager’s Journal on Cloud Computing, 2(1), 32-41. https://doi.org/10.26634/jcc.2.1.3304

Abstract

In today's fast growing world, everyone needs a secure storage for their important information like personal videos. So the service provider needs an excellent cloud video server with high security. But hackers easily hack any kind of security mechanism. In this paper , the novel tolerant security mechanism is proposed. A server stores all new information in its storage. The Cloud Video Server is acting as a controller of all the slaves. This system has two protocols named Promise Protocol and Review Protocol for monitoring slaves activity. This scheme uses two standard algorithms namely SHA3 (Secure Hash Algorithm -3 ) and Advanced Encryption Standard (AES) for security test. The performance is verified with the help of the metrics like security level, time complexity ,and Quality of Storage. The performance of this system is superior than the other schemes.

References

[1]. D.Kesavaraja, R.Balasubramanian and D.Sasireka, (2010). “Implementation of a Cloud Data Server (CDS) for Providing Secure Service in E-Business”, International Journal of Database Management Systems ( IJDMS ), Vol. 2, No. 2, pp. 44-55.

[2]. Saidane, Ayda; Nicomette, V.; Deswarte, Y.,(2009). “The Design of a Generic Intrusion-Tolerant Architecture for Web Servers,” IEEE Transactions on Dependable and Secure Computing, Vol.6, No.1, pp.45-58.

[3]. Wen-HsingKuo; Wanjiun Liao; Tehuang Liu,(2011). “Adaptive Resource Allocation for Layer-Encoded IPTV Multicasting in IEEE 802.16 WiMAX Wireless Networks,” IEEE Transactions on Multimedia, Vol.13, No.1, pp.116 -124.

[4]. J.-R. Ohm (1998).“Description of core experiments in MPEG-4 video,” ISO/IEC JTCI/SC29/WG11, N2554.

[5]. (2006). Mean Opinion Score (MOS) Terminology, 2006/07, ITU-T Rec. P.800.1.

[6]. Z. Wu and J. M. Boyce, (2007). “Adaptive error resilient video coding based on redundant slices of H.264/AVC,” in Proc. IEEE ICME, pp. 2138–2141.

[7]. P. Baccichet, S. Rane, A. Chimienti, and B. Girod, (2007). “Robust lowdelay video transmission using H.264/AVC redundant slices and flexible macroblock ordering,” in Proc. IEEE ICIP, Vol. 4, pp. 93–96.

[8]. Tillo, T.; Grangetto, M.; Olmo, G.,(2008). “Redundant Slice Optimal Allocation for H.264 Multiple DescriptionCoding,’ ,IEEE Transactions on Circuits and Systems for Video Technology, Vol.18, No.1, pp.59-70.

[9]. Kishore Ramachandran, LiviuIftode and Ajay Mohindra, “VaaS: Video as a Service on the Cloud” [Online]. Available: http://nsfcloud2011.cs.ucsb.edu /papers/Ramachandran_Paper.pdf.

[10]. [Online]. Available: http://www.csc.com/ managed_network_services/offerings/44522/51853- video_as_a_service_vaas.

[11]. [Online]. Available: http://www.mistralsolutions.com /hs-downloads/tech-briefs/dec09-article-3.html.

[12]. [Online]. Available: http://en.wikipedia.org /wiki/MVaaS.

[13]. [Online]. Available: http://en.wikipedia.org /wiki/Comparison_of_video_hosting_services.

[14]. Guurler, C.G.; Goorkemli, B.; Saygili, G.; Tekalp, A.M.,(2011). ”Flexible Transport of 3-D Video Over Networks,” Proceedings of the IEEE, Vol.99, No.4, pp. 694- 707.

[15]. Ellen Messmer (2010). ”Security of virtualization, cloud computing divides IT and security pros”, Network World, [Online]. Available:http://www.networkworld.com/article/2244954/virtualization/secu rity-of-virtualization-- cloud-computing-divides-it-and-security-pros.html.

[16]. (2014). ”SHA-3 Standard: Permutation-Based Hash and Extendable-Output Functions", DRAFT FIPS PUB 202 ,federal Information Processing Standards Publication ,[Online]. Available: http://csrc.nist.gov/ publications/drafts/fips-202/fips_202_draft.pdf.

[17]. (2012).”Tentative Timeline of the Development of New Hash Functions”, National Institute of Standards and Technology (NIST), Information Technology Laboratory [Online]. Available: http://csrc.nist.gov/ groups/ST/hash/timeline.html"NIST Selects Winner of Secure Hash Algorithm (SHA-3) Competition". NIST.

[18]. Guido Bertoni, Joan Daemen, Michael Peeters and Gilles Van Assche, (2011). “The Keccak sponge function family:Specifications summary “ Ref:http:// keccak.noekeon.org/specs_summary.html .

[19]. (2015). “Cryptographic hash function“, [Online]. Available:http://en.wikipedia.orgwiki/Cryptographic _hash_function.

[20]. Guido Bertoni, Joan Daemen,MichaelPeeters and Gilles Van Assche, (2007). “Sponge Functions”, ECRYPT Hash Workshop, [Online].

[21]. Guido Bertoni, Joan Daemen, Michael Peeters and Gilles Van Assche,(2008). “On the Indifferentiability of the Sponge Construction”, EuroCrypt.

[22]. “Keccak implementation overview Version 3.2” [Online]. Available:http://keccak.noekeon.org/Keccakimplementation- 3.2.pdf

[23]. Guo, Xu; Huang, Sinan; Nazhandali,Leyla; Schaumont, Patrick, (2010). “Fair and Comprehensive Performance Evaluation of 14 Second Round SHA-3 ASIC Implementations”, NIST 2nd SHA-3.

[24]. (2001). TREC Video Retrieval Test Collection [Online].Available:http://www.open-video.org/collection_detail. php?cid=7.

[25]. Kesavaraja, D.; Shenbagavalli, A.,(2013). “Cloud video as a Service [VaaS] with storage, streaming, security and Quality of service: Approaches and directions”, International Conference on Circuits, Power and Computing Technologies (ICCPCT), pp.1093-1098.

[26]. Online]. Available:http://en.wikipedia.org /wiki/Precision_and_recall.

[27]. Powers, David M. W (2011). “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness& Correlation”, Journal of Machine Learning Technologies, pp. 37–63.

[28]. Perruchet, P.; Peereman, R. (2004).“The exploitation of distributional information in syllable processing”. J. Neurolinguistics, pp. 97–119.

[29]. Fawcett, Tom (2006). “An Introduction to ROC Analysis”, Pattern Recognition Letters, pp. 861 – 874.

[30]. Olson, David L.; and Delen, Dursun (2008). Advanced Data Mining Techniques, Springer, 1st edition (February 1, 2008), pp.138.

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

Current Issue Vol. 10 Issue 2

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Volume 2 Issue 1 November - January 2015

Survey on Emerging Technologies for Secure Computations of Big Data

Madhusudhana Nooka Reddy* , C. Naga Raju**

* Associate Professor, Department of Computer Science and Engineering,Syamala Devi Institute of Technology for Women, Andhra Pradesh, India. ** Associate Professor and Head, Department of Computer Science and Engineering, Yogi Vemana University, Andhra Pradesh, India.

Reddy, N. N., and Raju, C. N. (2015). Survey on Emerging Technologies for Secure Computations of Big Data. i-manager’s Journal on Cloud Computing, 2(1), pp. 1-6. https://doi.org/10.26634/jcc.2.1.3300

Abstract

References

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Study of Cloud Based Mobile Learning Approaches

S.Vasundara* , D. Venkatesh**, D. Raghavaraju***

Vasundara, S., Venkatesh, D., and Raghavaraju, D. (2015). Study of Cloud Based Mobile Learning Approaches. i-manager’s Journal on Cloud Computing, 2(1), 7-13. https://doi.org/10.26634/jcc.2.1.3301

Abstract

References

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Research Issues in Enterprise Cloud Computing

S. Anandamurugan*

Assistant Professor (SLG), Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India

Anandamurugan, S. (2015). Research Issues in Enterprise Cloud Computing. i-manager’s Journal on Cloud Computing, 2(1), 14-23. https://doi.org/10.26634/jcc.2.1.3302

Abstract

References

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Multi Layer Encryption using Access Control in PublicClouds

K.Nethaji Sundar Sukumar* , L. Venkateswara Reddy**

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India. ** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India.

Sukumar, K. N. S. S., and Reddy, L. V. (2015). Multi Layer Encryption using Access Control in Public Clouds. i-manager’s Journal on Cloud Computing, 2(1), 24-31. https://doi.org/10.26634/jcc.2.1.3303

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Cloud Video Server (CVS): Secure Video Storage on Cloud using Heterogeneous Slaves

D. Kesavaraja* , A.Shenbagavalli**

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

Kesavaraja, D., and Shenbagavalli, A. (2015). Cloud Video Server (CVS): Secure Video Storage on Cloud using Heterogeneous Slaves. i-manager’s Journal on Cloud Computing, 2(1), 32-41. https://doi.org/10.26634/jcc.2.1.3304

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* Associate Professor, Department of Computer Science and Engineering,Syamala Devi Institute of Technology for Women, Andhra Pradesh, India.

** Associate Professor and Head, Department of Computer Science and Engineering, Yogi Vemana University, Andhra Pradesh, India.

S.Vasundara* , D. Venkatesh, D. Raghavaraju*

* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India.

** Professor, Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Andhra Pradesh, India.

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

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