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In the traditional index coding problem, a server employs coding to sendmessages to $n$ clients within the same broadcast domain. Each client alreadyhas some messages as side information and requests a particular unknown messagefrom the server. All clients learn the coding matrix so that they can decodeand retrieve their requested data. Our starting observation is that, learningthe coding matrix can pose privacy concerns: it may enable a client to inferinformation about the requests and side information of other clients. In thispaper, we mitigate this privacy concern by allowing each client to have limitedaccess to the coding matrix. In particular, we design coding matrices so thateach client needs only to learn some of (and not all) the rows to decode herrequested message. By means of two different privacy metrics, we first showthat this approach indeed increases the level of privacy. Based on this, wepropose the use of $k$-limited-access schemes: given an index coding schemethat employs $T$ transmissions, we create a $k$-limited-access scheme with$T_k\geq T$ transmissions, and with the property that each client needs at most$k$ transmissions to decode her message. We derive upper and lower bounds on$T_k$ for all values of $k$, and develop deterministic designs for theseschemes, which are universal, i.e., independent of the coding matrix. We showthat our schemes are order-optimal when either $k$ or $n$ is large. Moreover,we propose heuristics that complement the universal schemes for the case whenboth $n$ and $k$ are small.

Privacy in Index Coding: $k$-Limited-Access Schemes