  Edge caching (EC) decreases the average access delay of the end-users through
caching popular content at the edge network, however, it increases the leakage
probability of valuable information such as users preferences. Most of the
existing privacy-preserving approaches focus on adding layers of encryption,
which confronts the network with more challenges such as energy and computation
limitations. We employ a chunk-based joint probabilistic caching (JPC) approach
to mislead an adversary eavesdropping on the communication inside an EC and
maximizing the adversary's error in estimating the requested file and
requesting cache. In JPC, we optimize the probability of each cache placement
to minimize the communication cost while guaranteeing the desired privacy and
then, formulate the optimization problem as a linear programming (LP) problem.
Since JPC inherits the curse of dimensionality, we also propose scalable JPC
(SPC), which reduces the number of feasible cache placements by dividing files
into non-overlapping subsets. We also compare the JPC and SPC approaches
against an existing probabilistic method, referred to as disjoint probabilistic
caching (DPC) and random dummy-based approach (RDA). Results obtained through
extensive numerical evaluations confirm the validity of the analytical
approach, the superiority of JPC and SPC over DPC and RDA.
