Unconditionally Secure, Universally Composable Privacy Preserving Linear Algebra

@article{David2016UnconditionallySU,
  title={Unconditionally Secure, Universally Composable Privacy Preserving Linear Algebra},
  author={Bernardo Machado David and Rafael Dowsley and Jeroen van de Graaf and Davidson Marques and Anderson C. A. Nascimento and Adriana C. B. Pinto},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2016},
  volume={11},
  pages={59-73}
}
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… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 60 REFERENCES

Similar Papers

Loading similar papers…