BIG DATA SECURITY FOR AUTOMATIC TEMPER DETECTION

Cloud Computing And Big Data

Big data as well as other forms of digital resources have become more popular due to technical developments in recent times. Massive volumes of data are produced by big data, which may then be analyzed and put into practice in a wide range of technological and scientific fields. While big data has numerous practical uses, it also presents a number of obstacles in the areas of statistics, administration, and confidentiality and safety that must be overcome to enhance the customer experience. The big data is highly susceptible to increasing attacks and other types of manipulation by malicious users. This is highly problematic as the big data is very large and difficult to maintain and preserve to achieve effective understanding of the data that can be useful in achieving insightful information and knowledge. Improving the security of the big data can also lead to decrease in performance and a considerable delay in the processing of the queries on such data, therefore, there is a need for an effective mechanism that can increase the efficiency and the security of the big data at the same time. Therefore, this approach utilizes effective parallel computation for Map Reduce operations as well as forensic analysis for tamper detection through the use of bilinear pairing and recognition of the avalanche effect for report generation. The approach has been effectively tested for the purpose of understanding the effectiveness which has resulted in satisfactory outcomes.

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