{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T18:47:57Z","timestamp":1792090077551,"version":"4.4.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T00:00:00Z","timestamp":1792022400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Location-Based Services (LBS) generate large volumes of sensitive, location-dependent queries that must be processed under strict and often conflicting constraints of privacy and utility. Existing differential privacy (DP) mechanisms face a fundamental limitation: strong guarantees inject excessive noise and degrade utility, while utility-oriented methods under-protect users. We propose DT-ELR (Digital Twin-assisted Edge-Local Resolution), a framework that jointly optimises disclosure and retrieval utility through two tightly integrated algorithms and an on-device digital twin. Algorithm 1 (SensitivityClassifier) calibrates a monotone geo-indistinguishability budget from a data-derived sensitivity score, validated against a semantic venue-category labelling (AUC 0.81). The digital twin predicts from on-device signals alone whether releasing a query would improve its answer (AUC 0.90); self-sufficient queries are resolved against an on-device venue cache with no release and no budget spent. Algorithm 2 (ClosedFormAllocator) then distributes the remaining budget across released queries in closed form, matching the exact dynamic-programming optimum to within 1%. Experiments on three public check-in corpora \u2014 Foursquare-NYC, Gowalla and Brightkite \u2014 using an end-to-end nearest-venue retrieval task and three implemented adversaries, show that DT-ELR reduces optimal-inference localization success on sensitive queries from 61.3% to 11.4% on Foursquare-NYC at a matched mean budget, while raising end-to-end retrieval precision from 0.244 under full release to 0.559. Twin-guided release routing outperforms a tuned distance heuristic at every matched release rate, approaching an oracle ceiling of 0.565, and keeps 73% of sensitive queries entirely on the device. Component analysis confirms that each mechanism contributes independently, with edge-local resolution dominant, and the sensitivity model transfers to Gowalla and Brightkite without refitting. Against three recent location-perturbation mechanisms at a matched budget, DT-ELR attains a dominant privacy\u2013utility frontier on all three corpora: higher utility at equal disclosure, and lower disclosure at equal utility.<\/jats:p>","DOI":"10.1371\/journal.pone.0360633","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:49:25Z","timestamp":1792086565000},"page":"e0360633","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Digital twin-assisted edge-local query resolution for privacy\u2013utility trade-off in location-based services"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3142-192X","authenticated-orcid":true,"given":"Mohammed","family":"Hasan A.","sequence":"first","affiliation":[{"name":"Central University of South Bihar","place":["Gaya, Bihar, India"],"department":["Department of Computer Science"]},{"name":"University of Lahej","place":["Sabir, Lahej, Yemen"],"department":["Department of Computer Science"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nemi Chandra","family":"R.","sequence":"additional","affiliation":[{"name":"Central University of South Bihar","place":["Gaya, Bihar, India"],"department":["Department of Computer Science"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,15]]},"reference":[{"issue":"3","key":"pone.0360633.ref001","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1109\/TDSC.2019.2897675","article-title":"Privacy-preserving attribute-based keyword search in shared multi-owner setting","volume":"18","author":"Y Miao","year":"2021","journal-title":"IEEE Trans Dependable and Secure Comput"},{"issue":"6","key":"pone.0360633.ref002","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/TMC.2018.2861393","article-title":"Personalized privacy-preserving task allocation for mobile crowdsensing","volume":"18","author":"Z Wang","year":"2019","journal-title":"IEEE Trans on Mobile Comput"},{"issue":"7","key":"pone.0360633.ref003","doi-asserted-by":"crossref","first-page":"5827","DOI":"10.1109\/JIOT.2019.2952146","article-title":"Local differential privacy for deep learning","volume":"7","author":"PC Mahawaga Arachchige","year":"2020","journal-title":"IEEE Internet Things J"},{"key":"pone.0360633.ref004","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/TPDS.2013.45","article-title":"Privacy-preserving multi-keyword ranked search over encrypted cloud data","volume":"25","author":"N Cao","year":"2014","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"pone.0360633.ref005","doi-asserted-by":"crossref","unstructured":"Kandappu T, Misra A, Cheng S-F, Lau H-C. 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