An effective anonymization technique of big data using suppression slicing method
Nivedita Elanshekhar, Rajashree Shedge · 2017
Now a days there is a large collection of information and is being published in public network. This large data may contain personal information of a person. So, a difficulty in publishing the data of an individual to publish it without the information leak. To avoid the identification of an individual, security must be provided. Many anonymization techniques are used for the privacy of personal information. While publishing the data, techniques like anonymization using generalization and slicing failed to prevent membership disclosure and also has a linkage of information. This eventually led to the loss of utility. Slicing technique uses the horizontal and vertical partitioning for a perfect sepapration between the uncorrelated attributes to avoid the privacy exploitation. Suppression slicing has overcome this backlogs by comparing the attributes and tuples for similarity check and hide those data values to avoid the linkage and background attack. Thus an effective suppression slicing method is given, which are performed on the attributes having similar values for better utility and privacy.