A novel framework for optimised privacy preserving data mining using the innovative desultory technique

Computing is an ogle spectator witnessing afloat similar to a tornado. The chic data analysis and mining techniques compromise privacy and their exploitation have reached the pinnacle, demanding our attention. Amongst the available diverse techniques available for privacy preservation, the existing techniques involve only individual preservations. This work proposes novel three-tier architecture. The subsequent coalescing of three best secure techniques endows us with a three-fold privacy preservation, namely access control limitation technique, randomization and Privacy Preserving Clustering (PPC). Thus, our framework gives an efficient control system comprising authentication, authorization and access for each database application; efficiency and economical benefits of randomization and advantages of PPC. We have further proposed a method for computing the object-based dissimilarity for secure computation for all different attribute types. PPC uses a combination of CLARANS with Simulating Annealing in order to achieve increased privacy, efficient scaling and improvised performance.