A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

Author(s)	Mahendralal Prajapati, Alok Kumar Upadhyay, Mehdi Rezaie, Jyotshna Dongradive
Country	India
Abstract	In the Artificial Intelligence era, protecting individual users' data has become crucial. The collected data is stored in multiple databases having personally identifiable information (PII). This may provide a significant privacy concern for the database. Several privacy-preserving approaches have been proposed, including Differential Privacy, Homomorphic Encryption, Generative Adversarial Network and Federated Learning. In this paper, the above four anonymization techniques are compared. In addition, this study will review the strengths and weaknesses of these techniques. We also discuss the trade-off between data utility and privacy. The results of this study aim to guide researchers and practitioners in selecting suitable AI-driven anonymization techniques.
Keywords	AI-driven data Anonymization, Data privacy, Differential privacy, literature review, homomorphic encryption, Generative Adversarial Networks, Federated Learning
Field	Computer > Data / Information
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-18
Cite This	A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data - Mahendralal Prajapati, Alok Kumar Upadhyay, Mehdi Rezaie, Jyotshna Dongradive - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.24770
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.24770
Short DOI	https://doi.org/gt43vs

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