{
  "abstract": "Dynamic Searchable Symmetric Encryption (DSSE) enables keyword search over encrypted data without revealing plaintext to the server. Arabic morphological richness — where a single root generates dozens of surface forms — creates substantial challenges for encrypted search: unprocessed vocabularies inflate encrypted-index size and transmission cost, and limit retrieval recall by failing to match morphological variants. This paper presents the first empirically grounded preprocessor selection framework for Arabic DSSE, derived from a systematic evaluation of four Arabic preprocessing strategies — the Khoja stemmer, ISRI stemmer, Lucene Arabic Analyzer, and Farasa segmenter — against a normalization-only baseline within the Incidence Matrix DSSE (IM-DSSE) scheme on the Khaleej Arabic news corpus. We evaluate vocabulary size, search latency, search quality, and retrieval breadth on an annotated 1,400-document corpus using 56 benchmark queries, and assess scalability on cloud infrastructure across corpus sizes up to 45,500 documents. Stemming reduces vocabulary by up to 83% relative to the normalization-only baseline, proportionally reducing encrypted-index size and transmission cost. We find that per-query search latency is driven primarily by result-set size rather than vocabulary size: the normalization-only baseline is fastest per query because it matches the fewest documents, while preprocessors that broaden retrieval incur higher latency. At scale, all five configurations — including the normalization-only baseline — operate successfully to 45,500 documents, and the result-set-size effect on latency persists. In terms of search quality, light stemming and morphological segmentation achieve the best trade-off between retrieval precision and recall, while root-based stemmers sacrifice precision without commensurate recall gains. Based on these findings, the framework provides actionable, evidence-based guidelines mapping deployment requirements — memory scalability, search latency, search quality, and retrieval breadth — to concrete preprocessor choices for Arabic DSSE system designers.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kholoud Saad Al-Saleh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a1a7jqkvjjfn3.bib",
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  "doi": "10.1371/journal.pone.0351281",
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  "id": "a1a7jqkvjjfn3",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
    [
      "openalex",
      "1702|Cryptography and Data Security"
    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Memory/Memory recall"
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      "/Physical sciences/Mathematics/Cryptography/Encryption"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0351281&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/a1a7jqkvjjfn3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0351281",
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      "url": "https://doi.org/10.1371/journal.pone.0351281"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework",
  "title_text": "Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/a1a7jqkvjjfn3",
  "venue": "PLOS One",
  "versions": []
}