4 research outputs found
Private Information Retrieval with Private Noisy Side Information
Consider Private Information Retrieval (PIR), where a client wants to
retrieve one file out of files that are replicated in different servers
and the client selection must remain private when up to servers may
collude. Additionally, suppose that the client has noisy side information about
each of the files, and the side information about a specific file is
obtained by passing this file through one of possible discrete memoryless
test channels, where . While the statistics of the test channels are
known by the client and by all the servers, the specific mapping
between the files and the test channels is unknown
to the servers. We study this problem under two different privacy metrics.
Under the first privacy metric, the client wants to preserve the privacy of its
desired file selection and the mapping . Under the
second privacy metric, the client wants to preserve the privacy of its desired
file and the mapping , but is willing to reveal the
index of the test channel that is associated to its desired file. For both of
these two privacy metrics, we derive the optimal normalized download cost. Our
problem setup generalizes PIR with colluding servers, PIR with private
noiseless side information, and PIR with private side information under storage
constraints