{"paper":{"title":"Robustness of Maximal $\\alpha$-Leakage to Side Information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Flavio P. Calmon, Jiachun Liao, Lalitha Sankar, Oliver Kosut","submitted_at":"2019-01-21T22:41:31Z","abstract_excerpt":"Maximal $\\alpha$-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter $\\alpha$ determines the loss function used to measure the accuracy of a belief, ranging from log-loss at $\\alpha=1$ to the probability of error at $\\alpha=\\infty$. To study the effect of side information on this measure, we introduce and define conditional maximal $\\alpha$-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side informatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.07105","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}