Abstract
The modern encryption methods are reliable if strong keys (passwords) are used, but the human factor issue cannot be solved by cryptographic methods. The best variant is binding all authenticators (passwords, encryption keys, and others) to the identities. When a user is authenticated by biometrical characteristics, the problem of protecting a biometrical template stored on a remote server becomes a concern. The paper proposes several methods of generating keys (passwords) by means of the fuzzy extractors method based on signature parameters without storing templates in an open way.
Highlights
Information and knowledge are of great value and possess business potential
Several methods of generating key sequences based on fuzzy extractors are described that allow for the retrieval of random uniformly distributed bit sequences from biometric data [7]
This is caused by the differences between presented biometrical data and template data the error-correcting code is applied to the string; if a number of mismatched bits in a template and
Summary
Information and knowledge are of great value and possess business potential. When the level of IT development in society is increasing, the necessity of protecting information rises. Several methods of generating key sequences based on fuzzy extractors are described that allow for the retrieval of random uniformly distributed bit sequences from biometric data [7] This approach gives the owner the possibility of modifying secret biometric images at any time as well as a personal identifier (an authenticator, a key, etc.) binding with this image. Distances between some signature dots are normalized on the signature length in three-dimensional space (the third dimension is pen pressure on the tablet). The first approach to building an attribute space is based on a matrix of distances between the dots (readings) of the signature. Calculate the step: h = RN , where N is the number of dots resulting from the inverse Fourier d transform, and Rd is the desirable matrix dimension that is a multiple of the second power. Using a high number of features is meaningless because, with a smaller step, the number of features and the time of processing grow exponentially; the correlation dependence between features greatly increases
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