{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:04:02Z","timestamp":1784066642941,"version":"3.55.0"},"reference-count":0,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"4","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PoPETs"],"abstract":"<jats:p>Metric Differential Privacy (mDP) extends classical differential privacy (DP) by replacing Hamming adjacency with application-aware distance metrics, which offers utility-preserving protection for structured and continuous data including locations, trajectories, images, and text embeddings. This Systematization of Knowledge (SoK) paper synthesizes a decade of progress (2013-2025), clarifying mDP's foundations and its connections to central and local DP, and surveying three principal mDP mechanism families: homogeneous distance mechanisms, non-homogeneous distance mechanisms, and optimized perturbation mechanisms. We organize applications across geo-location privacy, text and embeddings, image and voice protection, graphs and network telemetry, and federated\/edge settings. We also surface open challenges, including robust composition and adversarial modeling, context-adaptive privacy, high-dimensional scalability, and principled geometry-aware trade-off bounds, and distill practical guidance for selecting metrics, mechanisms, and metrics of utility. The goal is a unified reference and roadmap for deploying scalable, utility-preserving metric privacy in real-world systems.<\/jats:p>","DOI":"10.56553\/popets-2026-0128","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T21:23:59Z","timestamp":1784064239000},"page":"415-437","source":"Crossref","is-referenced-by-count":0,"title":["SoK: Metric Differential Privacy in Theory and Practice"],"prefix":"10.56553","volume":"2026","author":[{"given":"Xinpeng","family":"Xie","sequence":"first","affiliation":[{"name":"University of North Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Yu","sequence":"additional","affiliation":[{"name":"University of North Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"University of North Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Science Tokyo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxi","family":"Qiu","sequence":"additional","affiliation":[{"name":"University of North Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"35752","published-online":{"date-parts":[[2026,10]]},"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T21:24:05Z","timestamp":1784064245000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2026\/popets-2026-0128.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["10.56553\/popets-2026-0128"],"URL":"https:\/\/doi.org\/10.56553\/popets-2026-0128","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]}}}