Visual Privacy Auditing with Diffusion Models

arXiv:2403.07588v1 Announce Type: cross
Abstract: Image reconstruction attacks on machine learning models pose a significant risk to privacy by potentially leaking sensitive information. Although defending against such attacks using differential privacy (DP) has proven effective, determining appropriate DP parameters remains challenging. Current formal guarantees on data reconstruction success suffer from overly theoretical assumptions regarding adversary knowledge about the target data, particularly in the image domain. In this work, we empirically investigate this discrepancy and find that the practicality of these …

arxiv attacks auditing cs.cr cs.lg current data defending differential privacy diffusion models image information machine machine learning machine learning models privacy risk sensitive sensitive information