Differential Privacy Technique for Privacy Preservation on Big Data
Johnny Antony P · International Journal for Research in Applied Science and Engineering Technology · 2019
Privacy of big data is most important factor for the enterprises so we should have more efficient methods to protect the data. In this article, we proposed a novel mechanism, called Adaptive Firefly Laplace Mechanism (AFLM), to preserve differential privacy on Big Data to protect sensitive information among analyst. We have many existing methods for privacy preservation but each method has its own limitation and drawbacks. To overcome the drawbacks of existing privacy preservation methods such as both cryptographic techniques and data anonymization are analyzed with differential privacy method in this article.Privacy has become crucial in knowledge based applications. Proper integration of individual privacy is essential for data mining operations. This privacy based data mining is important for sectors like Healthcare, Pharmaceuticals, Research, and Security Service Providers etc. There are many algorithms available for clustering the big data. This article focused on the density based clustering algorithms for first phase. Then our proposed algorithm called Adaptive Firefly Laplace Mechanism algorithm is used for both finding sensitive data on clustered output, and to add noisy data instead of sensitive data to hide sensitive information from analyst.