Abstract
Application domains like big data and IoT require a lot of user data collected and analyzed to extract useful information, and those data might include user's sensitive and personal information. Hence, it is strongly required to ensure the privacy of user data before releasing them in the public space. Since the fields of IoT and big data are constantly evolving with new types of privacy attacks and prevention mechanisms, there is an urgent need for new research and surveys to develop an overview of the state-of-art. We conducted a systematic mapping study on selected papers related to user privacy in IoT and big data, published between 2010 to 2021. This study focuses on identifying the main privacy objectives, attacks and measures taken to prevent the attacks in the two application domains. Additionally, a visualized classification of the existing attacks is presented along with privacy metrics to draw similarities and dissimilarities among different attacks.
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