International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Privacy-Preserving Search Systems: A Comprehensive Analysis of Advanced Techniques and Real-World Implementations

Author(s) Siddharth Pratap Singh
Country United States
Abstract This article presents a comprehensive article analysis of privacy-preserving search systems, examining the evolution of advanced approaches and their practical implementations across multiple domains. The article explores the integration of privacy-by-design principles in modern search architectures, focusing on data minimization strategies and encryption mechanisms throughout the search pipeline. The article investigates cutting-edge technologies including federated learning, differential privacy, and homomorphic encryption, demonstrating how these approaches enable robust privacy protection while maintaining search effectiveness. The analysis encompasses implementations across healthcare, legal discovery, and financial services sectors, providing insights into domain-specific challenges and solutions. The article demonstrates that contemporary privacy-preserving techniques can maintain high search quality while significantly enhancing privacy protections, challenging the traditional assumption that privacy and performance are inherently conflicting goals. This article contributes to the growing body of knowledge on privacy-enhanced information retrieval systems and provides practical insights for implementing privacy-preserving search technologies in sensitive data environments.
Keywords Keywords: Privacy-Preserving Search, Federated Learning, Information Retrieval Systems, Homomorphic Encryption, Data Privacy Protection
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-29
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.34087
Short DOI https://doi.org/g8xgn6
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E-ISSN 2582-2160
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