Data Transformation Technique for Preserving Privacy in Data - Erothi, Uma Shankar Rao; Rodda, Sireesha (International Journal of Computer Sciences and Engineering, 2025)
The increase of digitization has led to growing concerns over preserving privacy of sensitive data. The ubiquity of sensitive information in data sources such as financial transactions, commercial transactions, medical records, network communication etc., steered towards development of different privacy preserving techniques. In this paper, a novel data transformation technique has been proposed for providing efficient privacy preservation in the data. Inorder to provide privacy to data, the numeric attributes are transformed to the range [-1,1] while the characters or strings are transformed to binary strings. Data analysis over the transformed dataset provides the same result as that of the original dataset. The performance of the data transformation technique is evaluated on the datasets before and after transformation. Experiments on five standard datasets indicate high data utility of the proposed technique. The proposed technique is also evaluated on the standard network intrusion dataset NSL-KDD dataset to study the effectiveness of the proposed technique in intrusion detection domain and the results are analyzed. Privacy measures are evaluated to ascertain the degree of privacy offered by the proposed technique.