Privacy-Preserving Data Mining (PPDM), as an important branch of data mining and an interesting topic in privacy preservation, has gained special attention in recent years. In addition to extracting useful information and revealing patterns from large amounts of data, PPDM also protects private and sensitive data from disclosure without the permission of data owners or providers. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. K-anonymity is a property that models the protection of released data against possible re-identification of the respondents to which the data refers. Anonymization approach makes the data owners anonymous but vulnerable to attacks like linking attacks. The paper presents various techniques which are used to perform PPDM technique and also tabulates their advantages and disadvantages.

Anonymization, Privacy Preserving Data Mining, k-anonymity, Randomization

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