Fingerprint Protected Password Authentication Protocol

Abstract

With the rapid development of industrial Internet of things (IIOT), a variety of cloud services have been deployed to store and process the big data of IIOT. The traditional password only authentication is unable to meet the needs of security situation in IIOT. Therefore, a lot of mobile phone assisted password authentication schemes have been proposed. However, in existing schemes, the secret information is required to be stored in the user’s mobile phone. Once the phone is lost, the secret information may be obtained by the opponent, which will bring irreparable loss to the user. To address the above problems, we propose a fingerprint protected password authentication scheme which has no need to store the secret parameter in the mobile phone. When a user logs in, he uses his mobile phone to generate the private key which is used to decrypt the encrypted text generated during the registration phase. The process of generating the private key needs to enter the password and the fingerprint. When the computer interacts with the mobile phone, the user’s password will be blinded so that it can protect the user’s password from adversary’s attacks. Theoretical analysis and experimental results show that our scheme improves the security of the user’s secret. Meanwhile, our scheme can resist the opponent’s dictionary attacks, replay attacks, and phishing attack. Our scheme can reduce the storage pressure of the mobile phone and is easy to deploy.

PresSafe: Barometer-Based On-Screen Pressure-Assisted Implicit Authentication for Smartphones

“…[28] relies on vibration captures by accelerometer to reconstruct the user's heartbeat signal. [29] use user's fingerprint to assist in generating private keys. In [30], a dynamic feature selection for hand-writing is developed to enhance the authentication performance.…”

“…However, these solutions are still not perfect: signals used in these works are generated from biochemical effect [2], [26], [28], which are weak and are not directly measured thus needs a high signal-to-noise ratio environment; or are sent from other devices [27], which face common problems in machine-to-machine authentication, as in [31], [32]. The approaches in [29], [30] heavily rely on online servers and can hardly be performed in an offline manner.…”

Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

“…It usually uses learning methods to extract information representing user characteristics from different types of sensors on mobile devices to build a model. Such as face [6,7], fingerprint [8,9], voice [10,11], environmental location [12,13], keystroke behavior [14,15], finger movement [16,17], etc. However, the above-mentioned biometric information collection usually requires invoking the privacy-related permissions of the mobile device, which makes users worry that their privacy-related information may be leaked, and cannot take into account the security, privacy, and usability requirements of mobile authentication jointly at the same time.…”

Management patients information based finger print

“…Both fingerprint scanners and readers are incredibly secure and appropriate devices for security rather than a secret word because the password is easy to guess and difficult to remember. A fingerprint-protected password authentication technique that does not require the secret parameter to be saved in the phone [2]. Throughout the secret key generation process, the password and fingerprint should always be submitted.…”