Current Advancements of Biometric Systems De-identification

Journal of Biometrics & Biostatistics

article

Nujeti Bindhu¹ 

[1] Department of Computer Science, Vignan’s University

关键词: Biometric;    De-identification;    Social Lives;    Data Mining;    Big Data;   

DOI  :  10.37421/2155-6180.2022.13.93

来源: Hilaris Publisher

【 摘 要 】

We live in a culture where people's personal and social lives, professionalaffiliations, hobbies, and interests all become part of their public profile. Socialnetwork accounts or digital identities are a good example of how diverseaspects of a person's life become publicized. Decision making, informationfusion, artificial intelligence, pattern recognition, and biometrics all benefit fromthe complicated relationships between online personalities and our actualworld. In the information security arena, numerous research have examinedintelligent algorithms and information fusion techniques. Machine learningand deep learning breakthroughs have created new ways to extract newknowledge from publicly available data, posing new concerns to user privacy.The performance of existing biometric identification systems can be impactedby merging de-identification with various types of auxiliary information thatmay be provided directly or indirectly, according to this review paper. The deidentification of biometric data to ensure user privacy is discussed analytically.This article also includes an overview of existing and emerging biometricresearch, as well as several unresolved problems of critical importance toinformation privacy and security experts. In an increasingly linked society, theanswers to these concerns may aid in the creation of new biometric securityand privacy preservation solutions [1,2].

【 授权许可】

Unknown