It was recently observed in , 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 , 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.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Symposium on Information Theory, ISIT 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Aug 15 2018|
|Event||2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States|
Duration: Jun 17 2018 → Jun 22 2018
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Other||2018 IEEE International Symposium on Information Theory, ISIT 2018|
|Period||6/17/18 → 6/22/18|
Bibliographical noteFunding Information:
The work of the authors was partially funded by NSF under Awards 1423271, 1314937 and 1740047.