Index coding employs coding across clients within the same broadcast domain. This typically assumes that all clients learn the coding matrix so that they can decode and retrieve their requested data. However, learning the coding matrix can pose privacy concerns: it may enable clients to infer information about the requests and side information of other clients . In this paper, we formalize the intuition that the achieved privacy can increase by decreasing the number of rows of the coding matrix that a client learns. Based on this, we propose the use of k-limited-access schemes: given an index coding scheme that employs T transmissions, we create a fc-limited-access scheme with Tk ≤ T transmissions, and with the property that each client learns at most k rows of the coding matrix to decode its message. We derive upper and lower bounds on Tk for all values of k, and develop deterministic designs for these schemes for which Tk has an order-optimal exponent for some regimes.
|Original language||English (US)|
|Title of host publication||2017 IEEE Information Theory Workshop, ITW 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jan 31 2018|
|Event||2017 IEEE Information Theory Workshop, ITW 2017 - Kaohsiung, Taiwan, Province of China|
Duration: Nov 6 2017 → Nov 10 2017
|Name||IEEE International Symposium on Information Theory - Proceedings|
|Other||2017 IEEE Information Theory Workshop, ITW 2017|
|Country/Territory||Taiwan, Province of China|
|Period||11/6/17 → 11/10/17|
Bibliographical noteFunding Information:
The work of the authors was partially funded by NSF under Awards 1423271, 1527550 and 1314937.
The work of the authors was partially funded by NSF under Awards 1423271, 1527550 and 1314937. 1The coding matrix has size T × m and collects in each row the coding coefficients used for the corresponding broadcast transmission.