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Asp. Comput."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, and the like. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention.<\/jats:p>\n          <jats:p>Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, and so on. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on the fly. SecCT+ is a solution based on trusted execution environment while SecCT is a pure software solution. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT and SecCT+.<\/jats:p>","DOI":"10.1145\/3670697","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T11:09:26Z","timestamp":1717412966000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SecCT: Secure and Scalable Count Query Models on Encrypted Genomic Data"],"prefix":"10.1145","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3210-0714","authenticated-orcid":false,"given":"Yanguo","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9992-9253","authenticated-orcid":false,"given":"Rongqiao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-5159","authenticated-orcid":false,"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4370-6954","authenticated-orcid":false,"given":"Xiyue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0806-7746","authenticated-orcid":false,"given":"Luyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University, Xian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2616-2273","authenticated-orcid":false,"given":"Yaofeng","family":"Tu","sequence":"additional","affiliation":[{"name":"ZTE Corporation, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1093\/bib\/bbx139","article-title":"Privacy-preserving techniques of genomic data\u2013a survey","volume":"20","author":"Aziz Md. 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