Unconditionally Secure, Universally Composable Privacy Preserving Linear Algebra

Abstract

Linear algebra operations on private distributed data are frequently required in several practical scenarios (e.g., statistical analysis and privacy preserving databases). We present universally composable two-party protocols to compute inner products, determinants, eigenvalues, and eigenvectors. These protocols are built for a two-party scenario where the inputs are provided by mutually distrustful parties. After execution, the protocols yield the results of the intended operation while preserving the privacy of their inputs. Universal composability is obtained in the trusted initializer model, ensuring information theoretical security under arbitrary protocol composition in complex environments. Furthermore, our protocols are computationally efficient since they only require field multiplication and addition operations.