Tri Patternization On Generic Visualized Time Series Data
Beenu Ann Oommen · 2014
Time series is a set of observed data that are measured at regular intervals of time.The privacy of the time series data can be preserved through generalization and anonymity techniques. It has been useful in various fields of financial, medical and weather analysis. The data whose privacy is to be preserved is first identified and then generalization is made. The data will be generalized based on grouping algorithm and then the data will be displayed in an approximation format. The data will then be displayed in graphical format, providing no clue about the original data. This can be implemented using K-P anonymity algorithm for time series data with rich patterns.For ensuring privacy preservation and to eliminate pattern loss, a detectable noise will be incorporated with the data along with all the necessary features. The noise can be incorporated using data fly algorithm.