The Privacy Preserving Data Mining

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3.4 Generalization approach Generalization includes substituting a value along with a less definite but semantically constant value. Generalization can be reached via local recoding or global recoding[3, 5]. 3.5 Cryptographic technique In this ideal architecture the whole process is divided into the three main components the mediator, customer and a group of data service providers. Previously there is no interaction between the customer and the data provider. And when the client sends a query, the mediator forwards the information to all data holders and via exchange of the acknowledgements, the mediator generates the connection with the data providers[4]. There are various techniques suggested in the area of the Privacy Preserving Data Mining but one exceed over the other on the basis of different criteria. Algorithms are categorized based on utility, performance, cost, complexity, etc. It has been presented a tabular comparison in a chronological order. Table 1 represents different Merits and Demerits of the Various Techniques of PPDM. IV. CONCLUSION Currently, one of the major concerns is privacy to prevent the private data which they don’t want to share. This paper targeted on the previous literatures in the domain of Privacy Preserving Data Mining. From this analysis, it has been found that there is no only one method which is constant in all fields. All techniques operate in a separate way based on the pattern of data also the kind of domain or application.
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