All Dates/Times are Australian Eastern Standard Time (AEST)

Technical Program

Paper Detail

Paper IDD5-S2-T4.2
Paper Title Private Linear Transformation: The Joint Privacy Case
Authors Nahid Esmati, Anoosheh Heidarzadeh, Alexander Sprintson, Texas A&M University, United States
Session D5-S2-T4: Private Information Retreival III
Chaired Session: Friday, 16 July, 22:20 - 22:40
Engagement Session: Friday, 16 July, 22:40 - 23:00
Abstract In this paper, we introduce the problem of Private Linear Transformation (PLT). This problem includes a single (or multiple) remote server(s) storing (identical copies of) $K$ messages and a user that wants to compute $L$ linear combinations of a $D$-subset of these messages by downloading the minimum amount of information from the server(s) while protecting the privacy of the entire set of $D$ messages. This problem generalizes the private information retrieval and private linear computation problems. In this work, we focus on the single-server case. For the setting in which the coefficient matrix of the required $L$ linear combinations generates a Maximum Distance Separable (MDS) code, we characterize the capacity for all parameters $K, D, L$, where the capacity is defined as the supremum of all achievable download rates. In addition, we present lower and/or upper bounds on the capacity for the settings with non-MDS coefficient matrices and the settings with a prior side information.