{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:19:15Z","timestamp":1774534755380,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t              https:\/\/ror.org\/01h0zpd94","award":["62262020"],"award-info":[{"award-number":["62262020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Releasing structured microdata requires balancing utility and privacy under group-based disclosure risks. We propose AE-LRHMA, a hybrid anonymization framework that performs Mondrian-style hierarchical partitioning in an autoencoder-learned latent space and integrates local (k,e)-microaggregation. To explicitly control sensitive-value concentration and diversity within each equivalence class, we introduce a tunable constraint set consisting of k, a maximum sensitive proportion threshold, and an optional sensitive-entropy threshold (used as a hard gate when enabled and otherwise as a soft term in split scoring). The anonymized output is generated via standard interval\/set generalization in the original space. Experiments on Adult and Bank Marketing demonstrate that AE-LRHMA yields lower information loss and more stable group structures than representative baselines under comparable settings. We further report linkage-attack-oriented risk metrics to empirically characterize relative disclosure trends without claiming formal guarantees, such as differential privacy.<\/jats:p>","DOI":"10.3390\/e28040372","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:05:33Z","timestamp":1774451133000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Autoencoder-Enhanced Hierarchical Mondrian Anonymization via Latent Representations"],"prefix":"10.3390","volume":"28","author":[{"given":"Junpeng","family":"Hu","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China"},{"name":"College of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenwu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinan","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghui","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China"},{"name":"College of Intelligent Science and Engineering, Hubei University for Nationalities, Enshi 445000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aufschl\u00e4ger, R., Folz, J., M\u00e4rz, E., Guggumos, J., Heigl, M., Buchner, B., and Schramm, M. 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