A privacy risk assessment model for open data

While the sharing of information has turned into a typical practice for governments and organizations, numerous datasets are as yet not openly published since they may violate users’ privacy. The hazard on data protection infringement is a factor that regularly hinders the distribution of information and results in a push back from governments and organizations. Moreover, even published information, which may appear safe, can disregard client security because of the uncovering of users’ personalities. This paper proposes a privacy risk assessment model for open data structures to break down and diminish the dangers related with the opening of data. The key components are privacy attributes of open data reflecting privacy risks versus benefits exchanges-off related with the utilization situations of the information to be open. Further, these attributes are assessed using a decision engine into a privacy risk indicator value and a privacy risk mitigation measure. Privacy risk indicator expresses the anticipated estimation of data protection dangers related with opening such information and privacy risk mitigation measure expresses the estimations that should be connected on the information to evade the expected security risks. The model is exemplified through five genuine scenarios concerning open datasets.