Privacy-Preserving AI in Healthcare Cybersecurity

Dr. Lucky Ogoo
Nov 14, 2025
2 min read

Privacy-preserving AI in healthcare cybersecurity is a critical area of focus as the healthcare sector increasingly relies on data-driven technologies. Ensuring the security and privacy of sensitive patient information while leveraging AI capabilities is paramount.

Artificial Intelligence (AI) has emerged as a transformative force within the healthcare sector, offering unprecedented advancements in clinical diagnostics, predictive analytics, and personalised medicine. The ability of AI systems to process vast quantities of heterogeneous medical data presents opportunities for improving patient outcomes, enhancing operational efficiency, and enabling proactive disease

management. However, the integration of AI into healthcare ecosystems is accompanied by significant privacy and cybersecurity challenges, primarily due to the sensitive and personally identifiable nature of health data and the increasing threat of data breaches and algorithmic misuse.

This research investigates the intersection of privacy-preserving technologies and AI in the context of healthcare cybersecurity. Through a mixed-methods approach, this thesis draws on both primary data source including expert interviews, institutional surveys, and implementation case studies in hospital networks and secondary data sources such as peer-reviewed literature, regulatory documents, and publicly available datasets from health research consortia. The study evaluates and compares four key privacy-

preserving techniques: federated learning, differential privacy, homomorphic encryption, and secure multi-party computation. Each method is examined for its technical viability, computational scalability, and suitability for clinical deployment.

Importance of Privacy-Preserving AI

Data Sensitivity: Healthcare data is highly sensitive and subject to strict regulations.
Trust: Maintaining patient trust is essential for the adoption of digital health solutions.
Regulatory Compliance: Organisations must comply with data protection regulations to avoid penalties.

Techniques for Privacy Preservation

Federated Learning: A decentralised approach where models are trained across multiple devices without sharing raw data.
Homomorphic Encryption: Allows computations on encrypted data, ensuring that sensitive information remains confidential.
Differential Privacy: Adds noise to datasets to prevent the identification of individual records while still providing useful insights.

Challenges in Implementation

Complexity: Implementing privacy-preserving techniques can be technically challenging and resource-intensive.
Performance Trade-offs: There may be a trade-off between privacy and model performance or accuracy.
Regulatory Uncertainty: Rapidly evolving regulations can create uncertainty around compliance and best practices.

Case Studies

Patient Data Sharing: Hospitals using federated learning to share insights without compromising patient confidentiality.
Predictive Analytics: Utilising homomorphic encryption to analyze patient data for predicting disease outbreaks while preserving privacy.

Future Directions

Enhanced Collaboration: Increased collaboration between tech companies and healthcare providers to develop robust solutions.
Research and Development: Ongoing R&D to improve privacy-preserving technologies and their integration into existing systems.
Policy Development: Establishing clear policies and guidelines to support the ethical use of AI in healthcare.