Data Privacy: Strategies for Protecting Sensitive Data for OT using Artificial Intelligence

Abstract

The exponential rise of data in the digital age has enabled huge possibilities for innovation in every field. Although the data surge has its implications with respect to the protection of sensitive information, it also contains high privacy concerns on account of it. In this paper, we discuss how Artificial Intelligence (AI) can be used to better develop privacy-preserving techniques. Finally, we provide a general overview of how differential privacy, federated learning, homomorphic encryption, and anonymization techniques are being used to protect sensitive data with AI-driven mechanisms.
Through an analysis of existing frameworks and case studies, we illustrate the effectiveness of these AI strategies in mitigating privacy risks while maintaining data utility for analytical purposes. We support these AI strategies via an analysis of existing frameworks and case studies to prove the efficacy of these AI strategies in reducing privacy risk while maintaining data utility for analytic purposes. In the meantime, we also tackle the open challenges of proper trade-off between data privacy and AI property, i.e. computational overhead, algorithmic factuality and accountability. In this way, our study examines them and offers valuable insights as well as directions for future research about the privacy-preserving landscape. This document aims to contribute to the ongoing torsion on AI and privacy of data while proposing actionable strategies for AI researchers, AI practitioners, and AI policymakers seeking to ensure that sensitive data is in an increasingly connected world.