{
  "abstract": "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 — Foursquare-NYC, Gowalla and Brightkite — 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–utility frontier on all three corpora: higher utility at equal disclosure, and lower disclosure at equal utility.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Central University of South Bihar, Gaya, Bihar, India",
    "Department of Computer Science, University of Lahej, Sabir, Lahej, Yemen"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammed Hasan A."
    },
    {
      "affs": [
        1
      ],
      "name": "Nemi Chandra R."
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/670w95xb8j5kw.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360633",
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  "id": "670w95xb8j5kw",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0360633",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Privacy-Preserving Technologies in Data"
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      "plos",
      "/Biology and life sciences/Developmental biology/Twins"
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      "/Research and analysis methods/Simulation and modeling/Algorithms/Genetic algorithms"
    ],
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      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Decision making"
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    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Intelligence"
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  "openalex": "W7221896294",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360633&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/670w95xb8j5kw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0360633",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360633"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  ],
  "title": "Digital twin-assisted edge-local query resolution for privacy–utility trade-off in location-based services",
  "title_text": "Digital twin-assisted edge-local query resolution for privacy–utility trade-off in location-based services",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/670w95xb8j5kw",
  "venue": "PLOS One",
  "versions": []
}