  This paper considers the single-server Private Linear Transformation (PLT)
problem when individual privacy is required. In this problem, there is a user
that wishes to obtain $L$ linear combinations of a $D$-subset of messages
belonging to a dataset of $K$ messages stored on a single server. The goal is
to minimize the download cost while keeping the identity of every message
required for the computation individually private. The individual privacy
requirement implies that, from the perspective of the server, every message is
equally likely to belong to the $D$-subset of messages that constitute the
support set of the required linear combinations. We focus on the setting in
which the matrix of coefficients pertaining to the required linear combinations
is the generator matrix of a Maximum Distance Separable code. We establish
lower and upper bounds on the capacity of PLT with individual privacy, where
the capacity is defined as the supremum of all achievable download rates. We
show that our bounds are tight under certain divisibility conditions. In
addition, we present lower bounds on the capacity of the settings in which the
user has a prior side information about a subset of messages.
