Title :
Clustering based Anonymization for privacy preservation
Author :
Ghate, Rashmi B. ; Ingle, Rasika
Author_Institution :
Dept. CT, Y.C.C.E., Nagpur, India
Abstract :
While registering on social networking site, it is necessary to give the personal information; some of this information is sensitive and needed to be preserved. To sustain the privacy of user on a social network Anonymization technique is employed. In Anonymization approach individuals personal information is either mask or remove from the dataset so individual´s data become anonymous. When a dataset is released it is important to prevent data from unwanted disclosure, balance the usefulness and privacy of published dataset. Proposed work gives the Anonymized view of a data set and the result of implementation of the single pass k-means Anonymization algorithm. To Anonymized the dataset generalization and suppression approaches are used.
Keywords :
data privacy; pattern clustering; security of data; social networking (online); clustering based anonymization; dataset generalization; dataset suppression; privacy preservation; single pass k-means anonymization algorithm; social network anonymization technique; social networking site; Business; Clustering algorithms; Data privacy; Privacy; Publishing; Security; Social network services; Anonymization; Generalization; K-Means; Social Network; Suppression;
Conference_Titel :
Pervasive Computing (ICPC), 2015 International Conference on
Conference_Location :
Pune
DOI :
10.1109/PERVASIVE.2015.7087176