Privacy in Index Coding: Improved Bounds and Coding Schemes

It was recently observed in [1], that in index coding, learning the coding matrix used by the server can pose privacy concerns: curious clients can extract information about the requests and side information of other clients. One approach to mitigate such concerns is the use of \(k\)-limited-access...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-01
Main Authors: Karmoose, Mohammed, Song, Linqi, Cardone, Martina, Fragouli, Christina
Format: Article
Language:English
Subjects:
Clients
Coding
Mathematical analysis
Matrix methods
Privacy
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It was recently observed in [1], that in index coding, learning the coding matrix used by the server can pose privacy concerns: curious clients can extract information about the requests and side information of other clients. One approach to mitigate such concerns is the use of \(k\)-limited-access schemes [1], that restrict each client to learn only part of the index coding matrix, and in particular, at most \(k\) rows. These schemes transform a linear index coding matrix of rank \(T\) to an alternate one, such that each client needs to learn at most \(k\) of the coding matrix rows to decode its requested message. This paper analyzes \(k\)-limited-access schemes. First, a worst-case scenario, where the total number of clients \(n\) is \(2^T-1\) is studied. For this case, a novel construction of the coding matrix is provided and shown to be order-optimal in the number of transmissions. Then, the case of a general \(n\) is considered and two different schemes are designed and analytically and numerically assessed in their performance. It is shown that these schemes perform better than the one designed for the case \(n=2^T-1\).
ISSN:2331-8422