verification of data pattern for interactive privacy preservation model
The research problem of privacy-preserving data publishing is to release microdata in an aggregated form using distinguished techniques that will effectively conceal sensitive and private information but can be used by external users to exercise data mining. These techniques are often studied in interactive and non-interactive settings. While non-interactive setting mainly deals with the data publication using anonymization or noise addition approaches, interactive models are based on noisy response of queries. Most of the data pattern verification and classification accuracy determination approaches exist for non-interactively published microdata. In this paper, we verify the data pattern and determine classification accuracy on an interactive privacy preservation model called differential privacy . The contributions of this paper are: (1) We present a concise literature review of non-interactive and interactive models and technologies. (2) We propose an approach of retrieving information along with investigating, understanding and comparing the data classification accuracy experimentally on Privacy Integrated Queries . (3) We verify data pattern by comparing the correlation and classification accuracy of the differentially private data with non-interactive k -anonymous data.
privacy preservation data mining differential privacy