Privacy-First AI Models That Detect Fraud without Compromising Your Confidential Health Data

Authors: Puneet Sharma

DOI: https://doi.org/10.5281/zenodo.14615506

Short DOI: https://doi.org/g8x3qc

Country: USA

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Abstract: The rise of digital health platforms has significantly enhanced the delivery of medical services and patient care. However, it has also introduced new challenges, particularly in maintaining patient privacy and security in the face of increasing cyber threats. One of the most pressing concerns is fraud, ranging from identity theft to billing fraud, which puts confidential health data at risk. Artificial Intelligence (AI) has proven to be a powerful tool in fraud detection, but traditional AI systems often require access to sensitive data, raising concerns about privacy. This white paper explores the concept of privacy-first AI models in the context of fraud detection in healthcare, emphasizing the need for models that protect confidential health data while still enabling effective fraud detection. We will delve into privacy-preserving AI techniques such as federated learning, homomorphic encryption, and differential privacy, and examine how they can be used to detect fraud without compromising the integrity and confidentiality of health data. Additionally, this paper will explore the ethical and regulatory challenges surrounding AI in healthcare, offering best practices for ensuring compliance with privacy laws such as HIPAA and GDPR.

Keywords: Privacy-First AI, Healthcare Fraud Detection, Privacy-Preserving AI, Federated Learning, Homomorphic Encryption, Differential Privacy, Confidential Health Data, HIPAA, GDPR, AI in Healthcare.

Paper Id: 232001

Published On: 2020-12-07

Published In: Volume 8, Issue 6, November-December 2020

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