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In this work, we propose a quantum\u2010enhanced zero\u2010knowledge healthcare compression network (QZ\u2010HCN) that associates zero\u2010knowledge proofs (ZKPs) with quantum\u2010inspired deep learning (QIDL) by introducing an innovative adaptive quantum\u2010supported ZKP verification mechanism (AQ\u2010ZKV) and a quantum fusion autoconventional neural network (QF\u2010AutoCNN) technique to achieve efficient, privacy\u2010preserving compression. For healthcare IoT datasets, QZ\u2010HCN can reach 98.16% in accuracy, 97.09% in F\u2010measure, 96.32% in precision and 97.45% in recall, with a throughput of 449.57\u2009bits\/s; processing time is reduced to 0.85\u2009s, and memory cost is minimised to be only 192 kbits, which outperforms CNN\u2010Encryption (90.23% accuracy), proxy re\u2010encryption and homomorphic encryption by at most 13 percentage points in accuracy and 75 percentage points in memory efficiency. The secure and scalable management for CHI data is achieved by QZ\u2010HCN, which solves the problems of privacy threats and space costs of real\u2010time medical applications.<\/jats:p>","DOI":"10.1155\/int\/5062735","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:21:08Z","timestamp":1777544468000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantum\u2010Enhanced Zero\u2010Knowledge Compression Used for Cloud IoT Healthcare: A Scalable, Privacy\u2010Preserving QZ\u2010HCN Framework"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2153-6894","authenticated-orcid":false,"given":"Rajasekaran","family":"P.","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5913-7955","authenticated-orcid":false,"given":"Duraipandian","family":"M.","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4956-5999","authenticated-orcid":false,"given":"Johny Renoald","family":"Albert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9299-9286","authenticated-orcid":false,"given":"R.","family":"Jamuna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2182-8184","authenticated-orcid":false,"given":"Usha","family":"Moorthy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-025-05854-4"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s44335-025-00040-6"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-021-05411-2"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13040687"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-026-35540-3"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-212189"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111519"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2026.3653815"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/app15116060"},{"key":"e_1_2_11_10_2","first-page":"338","article-title":"Blockchain-Enabled Secure Data Sharing for AI-Driven Diabetes Research and Personalized Treatment","volume":"8","author":"Jain P.","year":"2025","journal-title":"Vascular and Endovascular Review"},{"key":"e_1_2_11_11_2","first-page":"553","volume-title":"A Unified Framework for Non-universal Snarks","author":"Lipmaa H.","year":"2022"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/03772063.2026.2616254"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2024.1502745"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/fi17030107"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics14234609"},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21124223"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2026.3662774"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0301716"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2025.110723"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/app14010139"},{"key":"e_1_2_11_21_2","first-page":"1","volume-title":"Quantum Enhanced Machine Learning for Medical Image Analysis: A Hybrid Approach","author":"Rawas S.","year":"2026"},{"key":"e_1_2_11_22_2","doi-asserted-by":"crossref","unstructured":"SahC. 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