Next-Gen Cryptography: The Role of Machine Learning Applications in Privacy Preservation for Sensitive Data

Next-Gen Cryptography: The Role of Machine Learning Applications in Privacy Preservation for Sensitive Data

G. Padmapriya, V. Vennila, K. Anitha, N. Manikandan, M. Suresh Anand
Copyright: © 2024 |Pages: 21
DOI: 10.4018/979-8-3693-4159-9.ch010
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Abstract

In a time marked by an ever-increasing number of sensitive data and mounting worries about breaches of privacy, the area of cryptography has emerged as the frontrunner in the fight to keep personal information secure. “Next-Gen Cryptography: The Role of Machine Learning in Privacy Preservation for Sensitive Data” investigates the revolutionary junction of two fields that are on the cutting edge: cryptography and machine learning. This chapter elucidates how the combination of these disciplines promises to reshape the landscape of data security, particularly with regard to the protection of sensitive information.
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Background

In recent years, the integration of artificial intelligence (AI) into healthcare has surged, promising transformative advancements in patient care and medical outcomes. This survey aims to delve into the utilization of sensitive data within healthcare AI, recognizing its pivotal role in training robust algorithms and optimizing strategies. From assisting clinicians in diagnosing diseases to streamlining administrative tasks, AI technologies have demonstrated remarkable potential in enhancing healthcare delivery and patient outcomes. However, leveraging sensitive data presents significant challenges, including data privacy, security breaches, and regulatory compliance, such as the Health Insurance Portability and Accountability Act (HIPAA). Navigating these complexities while fostering innovation and collaboration poses a significant challenge for stakeholders in the healthcare AI ecosystem. Despite the challenges, the utilization of sensitive data in healthcare AI presents immense opportunities for improving patient outcomes, enhancing clinical decision-making, and optimizing healthcare delivery. Looking ahead, addressing the ethical, regulatory, and technical challenges associated with the use of sensitive data will be crucial in realizing the full potential of AI in transforming healthcare delivery.

In the realm of healthcare, artificial intelligence (AI) is opening up unprecedented possibilities, ushering in transformative advancements and addressing significant global healthcare challenges. This innovative solution successfully tackled the protein folding problem, a longstanding impediment to critical advancements in the fields of biology and medicine over past five decades (Sen et al., 2020).

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