**5. Conclusion**

Businesses and even governments collect data through many digital platforms (social media, e-health, e-commerce, entertainment, e-government etc.) they use to

#### *Privacy Preserving Data Mining DOI: http://dx.doi.org/10.5772/intechopen.99224*

serve their customers/citizens. The data collected can be sensitive data and this data can be stored, analyzed and, in good probability, anonymized and shared with others. In studies where data is used at any stage of the life cycle, regardless of the purpose, it is necessary to explain a privacy permission and the reason why the data should be accessed. Privacy Preserving Data Mining (PPDM) techniques are being developed to allow information to be extracted from data without disclosing sensitive information.

There is no single optimal PPDM technique for any stage of the data lifecycle. The PPDM technique to be applied varies according to the application requirements, such as the desired privacy level, data size and volume, tolerable information loss level, transaction complexity, etc. Because different application areas have different rules, assumptions and requirements regarding privacy.

In this chapter, the previously proposed PPDM techniques are examined in two sections. First section includes the methods suggested for collecting, cleaning, integration, selection and transformation phases of input data that will be subject to data mining and second section covers methods applied to processed data. Finally, attacks against the privacy of data mining applications are given in this chapter.
