A comparative study of identity theft protection frameworks enhanced by machine learning algorithms

Abstract

A data breach occurs when sensitive or confidential data is compromised, leading to the risk of identity theft and other criminal or financial outcomes associated with loss of privacy and confidentiality. The extreme proliferation of data sharing has paradoxically compounded the risk of identity theft. Legislative responses and preemptive questions raised about the ethical implications of sharing data in cybersecurity response cooling-off periods have sought to increase raises and calm those broached questions. Considering the relatively new psychological phenomenon of cognitive load, this postponing of legislation had a cooling-off effect on ethical implications. Even though such processes are considered as temporary, the additional burden created by cognitive load can lead to impulsive decision-making processes and closure, therefore limiting the ability of individuals to correctly evaluate the ethical implications. Such decisions may include the calculation of potential risk of identity theft when sharing or accessing sensitive or confidential data. Though the current regulatory environment encourages the implementation of freezing services as means of risk minimization, these services are not available in all circumstances. Moreover, such services require preemptive action should stakeholders predict the risk of data compromise.