Privacy-preserving edge caching: A probabilistic approach

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