Data collection and transaction is most important task for any kind of processing. Nowadays several techniques have been found to secure the data in its transaction. Privacy preserving is a major problem faced in the organization and health care centres which handle high volume of data. Anonymization works at data collection and transaction to remove the unwanted track or trail that occurs. Several anonymization techniques, such as generalization, suppression and bucketization have been designed for privacy preservation in microdata publishing. Existing techniques have various degree of complexity to ensure an individual’s privacy. It is proposed to introduce Distribution Factor (DF) measure on sensitive information. The distribution factor is of fuzzy type values and it can handle high-dimensional data. This helps in grouping the attribute more complex with numeric values. So there is a strong correlation between the attributes. This overcomes the privacy violations and provides better data utility than previous techniques. Distribution factor helps to integrate ontology based technique with the main application. It is useful to explore the strength of a privacy preserving techniques using ontology.