Time series data visualized using three patterns
K. Sri Ragavendra, L. Sherin Beevi, S. Sathishkumar · 2016
Privacy preserving techniques have been actively studied on the time-series data in various fields like financial, medical and weather analysis. I focused towards preserving the data through anonymity and generalization, to resist homogeneity attack. First investigate, what's the privacy to be incorporated in the time-series data and after finding the data which needs to be preserved various perturbation terminology were identified and worked out towards secure multi-party computation and encryption techniques in distributed computing. Here i focussed towards generalized technique in which the data will be filtered or generalized in a grouped structure based on time-series grouping algorithm and the data will be shown in the approximation format. So that the data won't get disclosed. The second technique involves the display of data in graphical format providing with no clue about the exact data and approximation techniques incorporates an exact preserving of data. The proposed system incorporates all the necessary features, In addition I am trying to incorporate security by adding a detectable noise to this time-series data.