Survey on Anonymization of Privacy Preserving in Data Publishing
Anushree Raj
, Rio G. L. D'Souza
Anonymization, Privacy Preserving, k-anonymity, PPDM, PPDP
Privacy-preserving data mining has numerous applications which are naturally supposed to be “privacy-violating” applications. The key is to design methods which continue to be effective, without compromising security. Data mining is the process of analyzing data. Data Privacy is collection of data and distribution of data. Privacy issues arise in different area such as health care, intellectual property, biological data, financial transaction etc. Protection of data is a very challenging task while data transfer. Sensitive information needs protection. There are two kinds of major attacks against privacy namely record linkage and attribute linkage attacks. Research have proposed some methods namely k-anonymity, ℓ-diversity, t-closeness for data privacy. k-anonymity method preserves the privacy against record linkage attack alone.
"Survey on Anonymization of Privacy Preserving in Data Publishing", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.3, Issue 11, page no.219 - 224, November-2018, Available :https://ijsdr.org/papers/IJSDR1811038.pdf
Volume 3
Issue 11,
November-2018
Pages : 219 - 224
Paper Reg. ID: IJSDR_180780
Published Paper Id: IJSDR1811038
Downloads: 000347175
Research Area: Engineering
Country: Mangaluru, Karnataka, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
