Using a broadcast channel to transmit clients' data requests may impose privacy risks. In this paper, we tackle such privacy concerns in the index coding framework. We show how a curious client can infer some information about the requests and side information of other clients by learning the encoding matrix used by the server. We propose an information-theoretic metric to measure the level of privacy and show how encoding matrices can be designed to achieve specific privacy guarantees. We then consider a special scenario for which we design a transmission scheme and derive the achieved levels of privacy in closed-form. We also derive upper bounds and we compare them to the levels of privacy achieved by our scheme, highlighting that an inherent trade-off exists between protecting privacy of the request and of the side information of the clients.
|Original language||English (US)|
|Title of host publication||2017 IEEE International Symposium on Information Theory, ISIT 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Aug 9 2017|
|Event||2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany|
Duration: Jun 25 2017 → Jun 30 2017
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Other||2017 IEEE International Symposium on Information Theory, ISIT 2017|
|Period||6/25/17 → 6/30/17|
Bibliographical noteFunding Information:
The work of the authors was partially funded by NSF under Awards 1423271, 1527550 and 1314937.