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being captured. Along these lines, it allows for targeting of multiple persons one after another. Moreover, it is a non-consensual and clandestine reconnaissance innovation. The proposed model, when brought to realization, could act as an effective tool for criminal identification by comparing a live capture or digital image to the stored face print in order to confirm an individual’s identity.
1.6 Future Scope
Biometrics pose danger to individual rights and privacy since technologies like facial recognition allow identification of citizens without their acknowledgement. Moreover, when consent is backed into the design of the technology, the privacy concerns regarding biometrics could be addressed [16].
Any modern technology is laden with concealed threats with no claim of infallibility either by the software maker, person selling it, or the one who advocates its deployment. In the context of criminal justice administration research, it indicates that images captured with default camera settings preferably expose fair complexion rather than dark, affecting results of Facial Recognition Technology across racial groups. One methodology might be to utilize a technology-neutral regulatory framework that identifies degrees of damages.
1.7 Conclusion
Biometric technologies have wide-ranging applications. They are being increasingly used every day for phone security, banks, and governments looking towards these technologies as security measures for verifying transactions. Important government organizations are using facial recognition technology to create databases using driver’s license and passport details for effective administration, socio-economic development, and law enforcement. The thought that refined innovation implies more prominent proficiency should be fundamentally dissected. As these technologies penetrate more and more into our everyday lives, it is imperative to know and be educated about them. A reasonable strategy with ample safeguards for data protection and privacy is the need of the hour.
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2
Privacy in Multi-Tenancy Cloud Using Deep Learning
Shweta Solanki1* and Prafull Narooka2
1 MDS University Ajmer, Ajmer, India
2 Department of Computer Science, Agrawal College, Merta City, Rajasthan
* Corresponding author: [email protected]
Abstract
There is a responsibility to maintain the privacy and security of data in the Cloud Computing environment. In present times, the need for privacy is increased due to frequent development in multi-tenant service based systems. As a system of growth increases, the requirement