Abstract: A method of processing semiotic data includes receiving semiotic data including at least one data set P, selecting a function h, and for at least one of each data set P to be collected, computing h(P), destroying data set P, and storing h(P) in a database, wherein data set P cannot be extracted from h(P). The method further includes selecting a private key/public key (K, k) once for all cases, one of destroying the private key K and sending the private key K to a trusted party, and choosing function h as the public encryption function corresponding to k.

Abstract: A method (as well as system and signal-bearing medium) of processing biometric data, includes receiving biometric data including a data set P, selecting a secure hash function h, and for each data set P to be collected, computing h(P), destroying the data set P, and storing h(P) in a database, wherein data set P cannot be extracted from h(P).

Abstract: A method of processing semiotic data includes receiving semiotic data including at least one data set P, selecting a function h, and for at least one of each data set P to be collected, computing h(P), destroying data set P, and storing h(P) in a database, wherein data set P cannot be extracted from h(P). The method further includes selecting a private key/public key (K, k) once for all cases, one of destroying the private key K and sending the private key K to a trusted party, and choosing function h as the public encryption function corresponding to k.

Abstract: A method, system, and data structure are provided which facilitate matrix multiplication with advantageous computational efficiency. The invention, as variously implemented as a processing system, method, or data structure in a recording medium such as a memory, has applicability to numerous fields, including linear programming, where a great deal of multiplication of large, sparse matrices is performed. The method of the invention includes the steps of creating a first submatrix block from non-zero terms of a sparse matrix, such that all of the terms within a given column of the submatrix block are form a respective column of the sparse matrix, creating a corresponding second index submatrix block of the same dimensions as the first block, such that each term of the second block identifies the position of the corresponding term of the first block within the sparse matrix, in terms of a row and column index.