A pristine clean Cabalistic foruity strategize based approach for Incremental data stream privacy preserving data mining

Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellished or marred with technology; confiscating more consideration in many data mining applications. We are focusing on information safety measures in order to preserve the individual´s privacy, so that no personal information can be gained by the hacker from the data. Under the modern state of affairs of technological developments which has eradicated the distinction of domain data kept in private and public; we are inadequate in expertise of protecting the individual privacy. With today´s scenario of data strewn globally, the records get incremented from various sources, which further masquerade a greater confrontation. In this paper we propose a new technique called Cabalistic fortuity strategize based approach for incremental data stream based PPDM. Our technique optimizes the privacy level by toughening the re-identification of original data without compromising the processing speed and data utility. Thus, it solves the re-identification predicament which is found in the conventional random projections. Here the encryption based random projection assigns secret keys to the positions of random matrix elements and not to the random numbers, (viz., where the random matrix is going to hold the random numbers). We have tackled two kinds of random sequences for generating the random sequences called determinist and indeterminist random sequences and encrypted it in a new way. And also we have proposed a projection based sketch for incremental data stream. We hope the proposed solution will tarmac way for investigation track and toil well according to the evaluation metrics including hiding effects, data utility, and time performance.