| Paper ID | D5-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.
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