Near-Optimal Constrained Padding for Object Retrievals with Dependencies

The sizes of objects retrieved over the network are powerful indicators of the objects retrieved and are ingredients in numerous types of traffic analysis, such as webpage fingerprinting. We present an algorithm by which a benevolent object store computes a memoryless padding scheme to pad objects before sending them, in a way that bounds the information gain that the padded sizes provide to the network observer about the objects being retrieved. Moreover, our algorithm innovates over previous works in two critical ways. First, the computed padding scheme satisfies constraints on the padding overhead: no object is padded to more than c x its original size, for a tunable factor c > 1. Second, the privacy guarantees of the padding scheme allow for object retrievals that are not independent, as could be caused by hyperlinking. We show in empirical tests that our padding schemes improve dramatically over previous schemes for padding dependent object retrievals, providing better privacy at substantially lower padding overhead, and over known techniques for padding independent object retrievals subject to padding overhead constraints.