Handling big data security and Service through bilinear pairing and parallel computation
Shivani Shivajirao Kaktikar, Sarika Vasantrao Bodake · International journal of advance research and innovative ideas in education · 2021
A new type of data, Big Data, has emerged as a result of the increased use of online services by a huge number of people. Recent years have seen an explosion in the interest in big data as a concept. Analyzing and properly processing large amounts of data is exceedingly challenging. It becomes a gigantic challenge to maintain the privacy of big data, which has an incredibly high velocity and volume which increases the possibility that mishandling of this data might lead to the theft of secret or sensitive information, which can be extremely troublesome in the case of an incident. Several techniques have been developed to counteract this impact, but they have not proven very effective. Because of this, a methodology has been developed that efficiently divides, classifies, and splits massive data query that are mapped for parallel computing on to the Mongo DB. The technique described in this research paper makes use of the idea of bilinear pairing through the application of hashing and the detection of avalanche effect for the purpose of tamper detection and forensic analysis for effective security report creation. The detailed experimentation has been performed to determine the performance metrics of the presented technique which has reached the desired outcomes.