@article{3043,
  author = {Anitha Patil},
  title = {Securing MapReduce Programming Paradigm in Hadoop, Cloud and Big Data Ecosystem},
  journal = {International Journal of Computational Linguistics Research},
  year = {2020},
  volume = {11},
  number = {3},
  doi = {https://10.6025/jcl/2020/11/3/87-96},
  url = {http://www.dline.info/jcl/fulltext/v11n3/jclv11n3_1.pdf},
  abstract = {In the wake of technologies like cloud computing, virtualization and big data, MapReduce is the new programming paradigm used for processing voluminous data known as big data. MapReduce computations take place in thousands of commodity computers associated with cloud. Thus it can exploits Graphics Processing Units (GPUs) associated with cloud with its parallel processing abilities. Enterprises in the real world are shifting from traditional computing to cloud computing and traditional data mining to big data analytics. The rationale behind this is the exponential growth of data. Storing and processing such data needs big data eco-system  ssociated with cloud computing. In this context, MapReduce programming model is supported by distributed programming frameworks like Hadoop. However, it is very challenging to secure MapReduce computations from malicious attacks. In the literature many secure cloud storage mechanisms are found. However, securing MapReduce programming paradigm in Hadoop and big data
eco-system is still to be explored. In this paper, we proposed an algorithm based on differential privacy to protect big data from malicious Mapper and Reducer. We built a prototype application to demonstrate proof of the concept. The result showed the utility of the proposed approach. },
}