A Comparative Study of Data Perturbation Using Fuzzy Logic to Preserve Privacy

The latest advances in the field of information technology have increased enormous growth in the data collection in this era. Individual's data are shared for business or legal reasons, containing sensitive information. Sharing data is a mutual benefit for business growth. The need to preserve privacy has become a challenging problem in privacy preserving data mining. In this paper we deal with a data analysis system having sensitive information. Exposing the information of an individual leads to security threats and could be harmful. The confidential attributes are perturbed or distorted using fuzzy logic. Fuzzy logic is used to protect individual's data to hide details of data in public. Data is owned by an authorized user, and applies distortion. The Authorized user having original dataset distorts numeric data using S-fuzzy membership function. This distorted data is published to the analyst, hiding the sensitive information present in the original data. The analysts perform data mining techniques on the distorted dataset. Accuracy is measured using classification and clustering techniques generated on distorted data is relative to the original, thus privacy is achieved. Comparison of various classifiers is generated on the original and distorted datasets.