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... 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. Read More Read Less

Abstract

[1] Uzuner, Ö., Solti, I., Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American
Medical Informatics Association, 2010, 17 (5) 514-518.
[2] Sun, W., Rumshisky, A., Uzuner, O. (2013). Evaluating temporal relations in clinical text: 2012 i2b2 challenge. Journal of
the American Medical Informatics Association 2013, 20 (5) 806-813.
[3] Bethard, S., Savova, G., Chen, W-T.,...

[1] Uzuner, Ö., Solti, I., Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American
Medical Informatics Association, 2010, 17 (5) 514-518.
[2] Sun, W., Rumshisky, A., Uzuner, O. (2013). Evaluating temporal relations in clinical text: 2012 i2b2 challenge. Journal of
the American Medical Informatics Association 2013, 20 (5) 806-813.
[3] Bethard, S., Savova, G., Chen, W-T., Derczynski, L., Pustejovsky, J., Verhagen, M. (2016). Semeval-2016 task 12: Clinical
tempeval. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016): 2016. 1052-1062.
[4] Collobert. R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P (2011. Natural language processing (almost)
from scratch. Journal of Machine Learning Research 2011, 12(Aug), 2493-2537.
[5] Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C. (2016). Neural architectures for named entity
recognition. arXiv preprint arXiv:160301360 2016.
[6] Ma, X., Hovy, E. (2016). End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:160301354.
[7] He, J., Wang, H. (2008). Chinese named entity recognition and word segmentation based on character. In: Proceedings of
the Sixth SIGHAN Workshop on Chinese Language Processing: 2008.
[8] Li, H., Hagiwara, M., Li, Q., Ji, H. (2014). Comparison of the Impact of Word Segmentation on Name Tagging for Chinese
and Japanese. In: LREC: 2014. 2532-2536.
[9] Zhang, Y., Yang, J. (2018). Chinese NER Using Lattice LSTM. arXiv preprint arXiv:180502023 2018.