A multiple-theoretical GDPR model of parameters for personal data

Abstract

Increasingly, individuals are providing access to personal information to the public. The risk to the rights and freedoms of individuals may result from the processing of personal data (accidental or intentional destruction, loss, modification, unauthorised disclosure or access etc.), which may lead to physical, material and non-material detriment. For minimising losses from violation by an organisation of the GDPR Regulation and for reducing the negative rating at the state level, it is important to develop methods and models that implement appropriate assessments. That is why the multiple-theoretical representation of the parameters of the GDPR Regulation in a form of a tuple model allows solving the current scientific and practical problem of formalizing the process of assessing the negative consequences of leakage of personal data caused by their processing. Developed GDPR-model and its hierarchical structure owing to its integrated theoretical-multiple representation of annual turnover, a number of sets that characterize the level, specificity, nature and recurrence of violations, harm reduction, compliance with measures, determining factor, degree of responsibility, level of cooperation, data categories, the method of detection, abidance by codes and preventive recommendations allows, in accordance with the provisions of the GDPR, to determine the set of input and output parameters to formalize the process of assessing the loss of personal data. Further, for the implementation of the abovementioned process, it is required the development of a method for assessing the negative consequences of personal data leakage in accordance with the provisions of the GDPR, which will allow determining the maximum and actual damage to an organization.