Private Polynomial Computation for Noncolluding Coded Databases
Obead, Sarah A. ; Lin, Hsuan-Yin ; Rosnes, Eirik ; Kliewer, Jörg
arXiv, Tome 2019 (2019) no. 0, / Harvested from
We consider private polynomial computation (PPC) over noncolluding coded databases. In such a setting a user wishes to compute a multivariate polynomial of degree at most $g$ over $f$ variables (or messages) stored in multiple databases while revealing no information about the desired polynomial to the databases. We construct two novel PPC schemes, where the first is a generalization of our previous work in private linear computation for coded databases. In this scheme we consider Reed-Solomon coded databases with Lagrange encoding, which leverages ideas from recently proposed star-product private information retrieval and Lagrange coded computation. The second scheme considers the special case of coded databases with systematic Lagrange encoding. Both schemes yield improved rates compared to the best known schemes from the literature for a small number of messages, while in the asymptotic case the rates match.
Publié le : 2019-01-29
Classification:  Computer Science - Information Theory
@article{1901.10286,
     author = {Obead, Sarah A. and Lin, Hsuan-Yin and Rosnes, Eirik and Kliewer, J\"org},
     title = {Private Polynomial Computation for Noncolluding Coded Databases},
     journal = {arXiv},
     volume = {2019},
     number = {0},
     year = {2019},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1901.10286}
}
Obead, Sarah A.; Lin, Hsuan-Yin; Rosnes, Eirik; Kliewer, Jörg. Private Polynomial Computation for Noncolluding Coded Databases. arXiv, Tome 2019 (2019) no. 0, . http://gdmltest.u-ga.fr/item/1901.10286/