Technical Program

Paper Detail

Paper Title Private Polynomial Computation from Lagrange Encoding
Paper IdentifierWE2.R6.4
Authors Netanel Raviv, California Institute of Technology, United States; David Karpuk, Universidad de los Andes, Colombia
Session Private Computation II
Location Sorbonne, Level 5
Session Time Wednesday, 10 July, 11:40 - 13:20
Presentation Time Wednesday, 10 July, 12:40 - 13:00
Manuscript  Click here to download the manuscript
Abstract Private computation is a generalization of private information retrieval, in which a user is able to compute a function on a distributed dataset without revealing the identity of that function to the servers that store the dataset. In this paper it is shown that Lagrange encoding, a recently suggested powerful technique for encoding Reed-Solomon codes, enables private computation in many cases of interest. In particular, we present a scheme that enables private computation of polynomials of any degree on Lagrange encoded data, while being robust to Byzantine and straggling servers, and to servers that collude in attempt to deduce the identities of the functions to be evaluated. Moreover, incorporating ideas from the well-known Shamir secret sharing scheme allows the data itself to be concealed from the servers as well. Our results extend private computation to non-linear polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation.