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.
Periodicity:November - January'2015
DOI : 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.

Keywords

Big Data, Big Data Mining, Distributed Programming Frameworks, Secure Computations.

How to Cite this Article?

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

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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