Enhanced Time Series Pattern Based Effective Noise Generation For Privacy Protection On Cloud
Ashwini H Gajare · Zenodo (CERN European Organization for Nuclear Research) · 2015
Cloud computing can manage various IT resources and provide virtual scalable IT services under its openness and virtualization features. Hen ce,cloud customers can save huge capital investments in their own infrastructure by deployin g or utilizing these IT services through cloud. Due to the openness and virtualization,vari ous malicious service providers may exist in these cloud environments,and some of them may r ecord service data from a customer and then collectively deduce the customer�s private inf ormation without permission. Therefore,from the perspective of cloud customers,it is esse ntial to take certain technical actions to protect their privacy at client side. Noise obfusca tion is an effective approach in this regard by utilizing noise data. For instance,noise service r equests can be generated and injected into real customer service requests so that malicious se rvice providers would not be able to distinguish which requests are real ones if these r equests occurrence probabilities are about the same,and consequently related customer privacy can be protected. Currently,existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case,the probabi lity fluctuation could not be concealed by existing noise generation strategies,and it is a s erious risk for the customer�s privacy. To address this probability fluctuation privacy risk,we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First,we analyze this privacy risk and present a novel cluster based algo rithm to generate time intervals dynamically. https://www.ijiert.org/paper-details?paper_id=140451