Skip to main content
placeholder image

Mining the statistical information of confidential data from noise-multiplied data

Conference Paper


Abstract


  • Protecting data privacy and mining statistical information from protected data are the essential issues in big data. Protecting data privacy through noise-multiplied data is one of approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on micro noise-multiplied data.We show an application of the B-M L2014 Approach and demonstrates that the statistical information of the original data can be retrieved from their noise-multiplied data reasonably. The approach provides a new data mining technique for big data when data privacy is concerned.

Publication Date


  • 2017

Citation


  • Lin, Y. (2017). Mining the statistical information of confidential data from noise-multiplied data. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 1292-1297). United States: IEEE Computer Society.

Scopus Eid


  • 2-s2.0-85048092411

Start Page


  • 1292

End Page


  • 1297

Place Of Publication


  • United States

Abstract


  • Protecting data privacy and mining statistical information from protected data are the essential issues in big data. Protecting data privacy through noise-multiplied data is one of approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on micro noise-multiplied data.We show an application of the B-M L2014 Approach and demonstrates that the statistical information of the original data can be retrieved from their noise-multiplied data reasonably. The approach provides a new data mining technique for big data when data privacy is concerned.

Publication Date


  • 2017

Citation


  • Lin, Y. (2017). Mining the statistical information of confidential data from noise-multiplied data. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 1292-1297). United States: IEEE Computer Society.

Scopus Eid


  • 2-s2.0-85048092411

Start Page


  • 1292

End Page


  • 1297

Place Of Publication


  • United States