Unconditionally Secure, Universally Composable Privacy Preserving Linear Algebra

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. Th...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2016-01, Vol.11 (1), p.59-73
Main Authors: David, Bernardo, Dowsley, Rafael, van de Graaf, Jeroen, Marques, Davidson, Nascimento, Anderson C. A., Pinto, Adriana C. B.
Format: Article
Language:English
Subjects:
Computational efficiency
Computational modeling
Computer information security
Cryptography
Determinants
Eigenvalues
Eigenvalues and eigenfunctions
Eigenvectors
Linear algebra
Preserving
Privacy
Privacy Preserving
Protocols
Secure Computation
Trusted Initializer Model
UC security
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Summary: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.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2015.2476783