Privacy Preservation using Selective Colligation and Selective Scrambling

Abstract

Work aims to aid patients in monitoring their conditions and the patient details newlinecan be viewed only by the specified authority.Design of Colligation Strategy newlineAlgorithm (CSA) and Segmentation and Scrambling Algorithm (SSA) is used newlineto avoid the leakage of information.By using Colligation strategy,Classification newlinecan be done efficiently with no loss of information and Segmentation and newlineScrambling method consumes less time.The SSA Algorithm ensures that the newlinepatient details are secured with no loss of data. SSA strategy is used for newlinemaintaining the security of the data and to prevent membership disclosure and newlineto reduce the performance costs.The database is given to a trusted third party so newlinethat the useful or statistical information is obtained from the available newlineinformation. While providing the data to the third party, there is a high newlinepossibility that the identity of an individual may be revealed. The quasi newlineidentifiers are the group of attributes from which the identity of the person can newlinebe revealed. In the existing methods of privacy preservation, the database is newlinesliced, bucketized and the tuple inside the bucket are shuffled and the values are newlinesuppressed. The masking technique decreases the efficiency of classification and newlineconsumes a lot of time.To overcome the problems, CSA algorithm is introduced newlinein which Selective Colligation is done to certain tuples of an attribute.From the newlineColligated data, selected tuples are scrambled based on SSA algorithm.By newlineselective Colligation, classification can be done efficiently and by Selective newlineScrambling, less time is consumed. By CSA and SSA Algorithm, data can be fetched without the loss of information and less time is consumed.