An Efficient Method for Privacy Preserving and Data Truthful in Data Markets

Abstract

This paper contends that big data can have diverse attributes, which influence its quality. Contingent upon its cause, data handling innovations, and procedures utilized for data accumulation and logical disclosures, big data can have predispositions, ambiguities, and mistakes which should be recognized and represented to decrease induction blunders and enhance the exactness of produced bits of knowledge. Big data veracity is currently being perceived as an essential property for its usage, supplementing the three beforehand settled quality measurements (volume, Variety, and speed), But there has been little talk of the idea of veracity up to this point. This paper gives a guide to hypothetical and exact meanings of veracity alongside it are down to earth suggestions. We investigate veracity crosswise over three primary measurements: 1) objectivity/subjectivity, 2) truthfulness/ deception, 3) credibility/implausibility and propose to operationalize every one of these measurements with either existing computational instruments or potential ones, applicable especially to printed data examination. We consolidate the proportions of veracity measurements into one composite list – the big data veracity file. This recently created veracity record gives a valuable method for surveying efficient varieties in big data quality crosswise over datasets with printed data. The paper adds to the big data look into by arranging the scope of existing devices to quantify the recommended measurements, and to Library and Information Science (LIS) by proposing to represent heterogeneity of differing big data, and to recognize data quality measurements essential for each big data compose.