Discrete Wavelet Transform Based Cancelable Biometric System for Speaker Recognition

The biometric template characteristics and privacy conquest are challenging issues. To resolve such limitations, the cancelable biometric systems have been briefed. In this paper, the efficient cancelable biometric system based on the cryptosystem is introduced. It depends on permutation using a chaotic Baker map and substitution using masks in various transform domains. The proposed cancelable system features extraction phase is based on the Cepstral analysis from the encrypted speech signal in the time domain combined with the encrypted speech signal in the discrete wavelet transform (DWT). Then, the resultant features are applied to the artificial neural network for classification. Furthermore, wavelet denoising is used at the receiver side to enhance the proposed system. The cryptosystem provides a robust protection level of the speech template. This speech template can be replaced and recertified if it is breached. Our proposed system enables the generation of various templates from the same speech signal under the constraint of linkability between them. The simulation results confirmed that the proposed cancelable biometric system achieved higher a level of performance than traditional biometric systems, which achieved 97.5% recognition rate at low signal to noise ratio (SNR) of -25dB and 100% with -15dB and above

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

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

A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mathematical and Computer Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe 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