  This paper considers the single-server Private Linear Transformation (PLT)
problem with individual privacy guarantees. In this problem, there is a user
that wishes to obtain $L$ independent 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 each
message required for the computation individually private. The individual
privacy requirement ensures that the identity of each individual message
required for the computation is kept private. This is in contrast to the
stricter notion of joint privacy that protects the entire set of identities of
all messages used for the computation, including the correlations between these
identities. The notion of individual privacy captures a broad set of practical
applications. For example, such notion is relevant when the dataset contains
information about individuals, each of them requires privacy guarantees for
their data access patterns. We focus on the setting in which the required
linear transformation is associated with a maximum distance separable (MDS)
matrix. In particular, we require that the matrix of coefficients pertaining to
the required linear combinations is the generator matrix of an MDS 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 conditions.
