David Karpuk

Netanel Raviv, David A. Karpuk

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. In this paper it is shown that Lagrange encoding, a 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 colluding to 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 high degree polynomials and to data-privacy, and reveal a tight connection between private computation and coded computation.

Arsenia Chorti, Mehdi M. Molu, David Karpuk et al.

We investigate the possibility of developing physical layer network coding (PNC) schemes with embedded strong secrecy based on standard QAM modulators. The proposed scheme employs a triple binning approach at the QAM front-end of the wireless PNC encoders. A constructive example of a strong secrecy encoder is presented when a BPSK and an 8-PAM modulator are employed at the wireless transmitters and generalized to arbitrary M-QAM modulators, assuming channel inversion is attainable at the first cycle of the transmission. Our preliminary investigations demonstrate the potential of using such techniques to increase the throughput while in parallel not compromise the confidentiality of the exchanged data.