ElGamel Encryption for Biometric Database Protection

Roja, M. Mani; Sawarkar, Sudhir

doi:10.5120/11582-6914

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Because the database will block any attacker from unauthorized access and cross verify the attacker based on the validation of the ownership i.e., authentication code (AC) and sent a Twilio message to user for confirmation. In a current research study, the encryption-decryption algorithm renders more secure data protection storage (Maniroja & Sawarkar, 2013).…”

Section: Rationale Of the Studymentioning

confidence: 94%

“…Rosenberger (2018) discussed the valuation of biometric template protection mechanism based on a transformation. Maniroja and Sawarkar (2013) discussed biometric database safety using public key cryptography of biometric authentication systems. analyzed the methods and measures intended for safeguarding biometric fingerprint templates.…”

Section: Statement Of the Problemmentioning

confidence: 99%

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Development of secured algorithm to enhance the privacy and security template of biometric technology

Habibu¹

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The security of information and personal privacy are the growing concerns in today’s human life worldwide. The storage of biometric data in the database has raised the prospect of compromising the database leading to grave risks and misuse of the person’s privacy such as growth in terrorism and identity fraud. When a person’s biometric data stored is revealed, their security and privacy are being compromised. This research described a detailed evaluation on several outbreaks and threats associated with the biometric technology. It analyzed the user’s fear and intimidations to the biometric technology alongside the protection steps for securing the biometric data template in the database. It is known that, when somebody’s biometric data template is compromised from the database that consequently might indicate proof of identity robbery of that person. Mixed method to compute and articulate the results as well as a new tactic of encryption-decryption algorithm with a design pattern of Model View Template (MVT) are used for securing the biometric data template in the database. The model managed information logically, the view indicated the visualization of the data, and the template directed the data migration into pattern object. Factors influencing fear of biometric technology such as an exposer of personal information, improper data transfer, and data misuse are found. Strong knowledge of the ideal technology like the private skills of the biometric technology, data secrecy and perceived helpfulness are established. The fears and attacks along the technology like a counterfeit of documents and brute-force attack are known. The designed algorithm based on the cryptographic module of the Fernet keys instance are utilized. The Fernet keys are combined to generate a multiFernet key, integrated with biometric data to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and firmly stored in the database. The storage database has security measures that guard against an impostor’s attack. The database system can block the attacker from unauthorized access. Thus, significantly increased individual data privacy and integrity.

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Section: Rationale Of the Studymentioning

confidence: 94%

Section: Statement Of the Problemmentioning

confidence: 99%

Development of secured algorithm to enhance the privacy and security template of biometric technology

Habibu¹

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Biometric watermarks based on face recognition methods for authentication of digital images

Wójtowicz

Ogiela

2014

Security Comm Networks

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Because making digital images secure runs into the substantial challenge of owner authentication, many security schemes based on cryptography, steganography and watermarking technology include biometric recognition methods. To follow on these studies, this paper describes a combination of facial images with watermarking technology to automatically authenticate digital images owners/users. In the proposed methodology, biometric face recognition methods such as principal component analysis and eigenfeature regularization and extraction produce vectorial representations of facial images. These vectors are used as copyright watermarks, in a few common watermarking schemes, and are tested for identification purposes after they are extracted. Initially, watermarking algorithms are studied with some arbitrary cover image, and also the most robust algorithm is tested for different cover images of particular subjects. The strength of this paper is finding relationships between the original and extracted biometric data using neural networks instead of the most common, simple measures such as correlation coefficients or distance metrics. The NN subject identification is performed directly, as there is no need to reconstruct facial images after the watermarks are extracted, compute templates for particular subjects, or seek a suitable distance metric. What is more, the presented study includes a performance comparison of two machine learning methods, frequently used for face recognition, and of a few popular watermarking algorithms. Very promising identification results were obtained in many considered experiments, even those involving attacks on watermarked images. and watermarking [7,[15][16][17][18] are provided, because they now represent mainstream security technologies. This paper proposes a security model enabling the authentication of digital image owners using the watermarking

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ElGamel Encryption for Biometric Database Protection

Cited by 2 publications

References 8 publications

Development of secured algorithm to enhance the privacy and security template of biometric technology

Development of secured algorithm to enhance the privacy and security template of biometric technology

Biometric watermarks based on face recognition methods for authentication of digital images

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