Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework
- 1Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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.
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BibTeX
@article{alsaleh2026arabic,
title = {{Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework}},
author = {Kholoud Saad Al-Saleh},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0351281},
url = {https://doi.org/10.1371/journal.pone.0351281},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework
AU - Kholoud Saad Al-Saleh
PY - 2026
DA - 2026/09/21
JO - PLOS One
DO - 10.1371/journal.pone.0351281
UR - https://doi.org/10.1371/journal.pone.0351281
AB - 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.
SN - 1932-6203
ER -