A computer implemented method for matching an encrypted biometric input record with at least one stored encrypted biometric record, without data decryption of the input and the at least one stored record, the method comprising: providing an initial biometric vector to a neural network, wherein the neural network translates the initial biometric vector to a Euclidian measurable feature vector; storing the Euclidian measurable feature vector in a storage with other Euclidian measurable feature vectors; identifying, for each respective Euclidian measurable feature vector, a plurality of floating point values; using a bitmap to eliminate from an absolute distance calculation any of the plurality of values that are not present in every respective Euclidian measurable feature vector; receiving, from a mobile computing device over a data communication network, a current biometric vector representing the encrypted biometric input record; providing the current biometric vector to the neural network, wherein the neural network translates current biometric vector to a current Euclidian measurable feature vector; classifying the Euclidian measurable feature vector; and/or classifying the current Euclidian measurable feature vector, wherein the classifying is performed at least in part using one or more distance functions; and conducting a search of at least some of the stored Euclidian measurable feature vectors in a portion of the data storage using the current Euclidian measurable feature vector, wherein the encrypted biometric input record is matched with at least one encrypted biometric record in encrypted space as a function of an absolute distance computed between the current Euclidian measurable feature vector and a calculation of each of the respective Euclidian measurable feature vectors in the portion of the storage, wherein classifying the Euclidian measurable feature vector and/or the current Euclidian measurable feature vector returns floating point values, and a Frobenius algorithm is utilized to compute an absolute distance between each floating point and its average. providing an initial biometric vector to a neural network, wherein the neural network translates the initial biometric vector to a Euclidian measurable feature vector; storing the Euclidian measurable feature vector in a storage with other Euclidian measurable feature vectors; identifying, for each respective Euclidian measurable feature vector, a plurality of floating point values; using a bitmap to eliminate from an absolute distance calculation any of the plurality of values that are not present in every respective Euclidian measurable feature vector; receiving, from a mobile computing device over a data communication network, a current biometric vector representing the encrypted biometric input record; providing the current biometric vector to the neural network, wherein the neural network translates current biometric vector to a current Euclidian measurable feature vector; classifying the Euclidian measurable feature vector; and/or classifying the current Euclidian measurable feature vector, wherein the classifying is performed at least in part using one or more distance functions; and conducting a search of at least some of the stored Euclidian measurable feature vectors in a portion of the data storage using the current Euclidian measurable feature vector, wherein the encrypted biometric input record is matched with at least one encrypted biometric record in encrypted space as a function of an absolute distance computed between the current Euclidian measurable feature vector and a calculation of each of the respective Euclidian measurable feature vectors in the portion of the storage, wherein classifying the Euclidian measurable feature vector and/or the current Euclidian measurable feature vector returns floating point values, and a Frobenius algorithm is utilized to compute an absolute distance between each floating point and its average.