TRUSTe: an online privacy seal program
Communications of the ACM
Internet privacy concerns confirm the case for intervention
Communications of the ACM
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Differential Privacy for Clinical Trial Data: Preliminary Evaluations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
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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.