Confronting with growing size of data and pressure of extracting useful knowledge in different manners made privacy preserving a crucial subject. This major is even more important especially in big data environment that implements knowledge discovery and data mining for producing beneficial information. Beside the inner importance aspect of privacy of personal data, the efficiency of the approaches of preserving privacy is a special factor. This is because of the overheads that injected by privacy preserving methods in decreasing the accuracy of end results of data mining. Soft computing is a general name of a group of logic based methods that have several usages. Its recent usage is in privacy preserving in big data. In this paper, a comprehensive survey of different regular methods of privacy preserving for KDD and Data Mining presented and then reasons of why soft computing methods can be a substitute for Privacy Preserving in these environments are discussed. Beside the analysis and discussion of merit and shortcomings of approaches, a conceptual framework for state of the art of privacy-preserving represented and provides research gaps and future works.

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