{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T21:26:37Z","timestamp":1772227597998,"version":"3.50.1"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010668","name":"H2020 Leadership in Enabling and Industrial Technologies","doi-asserted-by":"publisher","award":["857202"],"award-info":[{"award-number":["857202"]}],"id":[{"id":"10.13039\/100010668","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3665980","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T21:21:23Z","timestamp":1771449683000},"page":"28435-28456","source":"Crossref","is-referenced-by-count":0,"title":["A Secure and Efficient LLM-Based System for Natural Language Query-to-SQL Translation in IoT Data Management"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4182-8376","authenticated-orcid":false,"given":"Mario San Emeterio De La","family":"Parte","sequence":"first","affiliation":[{"name":"Group of Next Generation Networks and Services (GRyS), Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0203-7440","authenticated-orcid":false,"given":"Yiting","family":"Wang","sequence":"additional","affiliation":[{"name":"Group of Next Generation Networks and Services (GRyS), Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7635-4564","authenticated-orcid":false,"given":"Jos\u00e9-Fern\u00e1n","family":"Mart\u00ednez-Ortega","sequence":"additional","affiliation":[{"name":"Group of Next Generation Networks and Services (GRyS), Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3333-4077","authenticated-orcid":false,"given":"N\u00e9stor Lucas","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Group of Next Generation Networks and Services (GRyS), Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación, Universidad Politécnica de Madrid (UPM), Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"issue":"12","key":"ref1","doi-asserted-by":"crossref","first-page":"7328","DOI":"10.1109\/TKDE.2025.3609486","article-title":"Next-generation database interfaces: A survey of LLMbased text-to-SQL","volume":"37","author":"Hong","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref2","author":"Tiwari","year":"2025","journal-title":"Tiwarikajal\/Seq2SQL\u2014Natural-Language- Sentences-to-SQL-Queries"},{"key":"ref3","volume-title":"Europe\u2019s Internet of Things Policy | Shaping Europe\u2019s Digital Future","year":"2024"},{"key":"ref4","volume-title":"THE 17 GOALS | Sustainable Development","year":"2025"},{"key":"ref5","volume-title":"Aggregate Farming in the Cloud | AFarCloud | Projekt | Fact Sheet | H2020","year":"2024"},{"key":"ref6","first-page":"42330","article-title":"Can LLM already serve as a database interface? A big bench for large-scale database grounded text-to-SQLs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.14778\/2735461.2735468"},{"key":"ref8","first-page":"78","article-title":"A rule based approach for NLP based query processing","volume-title":"Proc. 2nd Int. Conf. Electr. Inf. Commun. Technol. (EICT)","author":"Mahmud"},{"key":"ref9","article-title":"A survey on text-to-SQL parsing: Concepts, methods, and future directions","author":"Qin","year":"2022","journal-title":"arXiv:2208.13629"},{"key":"ref10","article-title":"Sequence to sequence learning with neural networks","author":"Sutskever","year":"2014","journal-title":"arXiv:1409.3215"},{"key":"ref11","article-title":"Seq2SQL: Generating structured queries from natural language using reinforcement learning","author":"Zhong","year":"2017","journal-title":"arXiv:1709.00103"},{"issue":"3","key":"ref12","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3390\/e25030513","article-title":"Improving text-to-SQL with a hybrid decoding method","volume":"25","author":"Jeong","year":"2023","journal-title":"Entropy"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1448"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.745"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-emnlp.89"},{"issue":"11","key":"ref16","first-page":"13067","article-title":"RESDSQL: Decoupling schema linking and skeleton parsing for text-to-SQL","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"37","author":"Li"},{"key":"ref17","first-page":"4171","article-title":"BERT: Pretraining of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Language Technol.","author":"Devlin"},{"issue":"140","key":"ref18","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref19","article-title":"A comprehensive evaluation of ChatGPT\u2019s zero-shot text-to-SQL capability","author":"Liu","year":"2023","journal-title":"arXiv:2303.13547"},{"key":"ref20","article-title":"From natural language to SQL: Review of LLM-based text-to-SQL systems","author":"Mohammadjafari","year":"2024","journal-title":"arXiv:2410.01066"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3737873"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129293","article-title":"Decoupling SQL query hardness parsing for text-to-SQL","volume":"621","author":"Yi","year":"2025","journal-title":"Neurocomputing"},{"key":"ref23","article-title":"SQLfuse: Enhancing text-to-SQL performance through comprehensive LLM synergy","author":"Zhang","year":"2024","journal-title":"arXiv:2407.14568"},{"issue":"24","key":"ref24","first-page":"25778","article-title":"CogSQL: A cognitive framework for enhancing large language models in text-to-SQL translation","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"39","author":"Yuan"},{"issue":"5","key":"ref25","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104136","article-title":"Optimizing text-to-SQL conversion techniques through the integration of intelligent agents and large language models","volume":"62","author":"Ojuri","year":"2025","journal-title":"Inf. Process. Manage."},{"issue":"3","key":"ref26","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103978","article-title":"Multi-pattern retrievalaugmented framework for text-to-SQL with Poincar\u00e9\u2013Skeleton retrieval and meta-instruction reasoning","volume":"62","author":"Guo","year":"2025","journal-title":"Inf. Process. Manage."},{"key":"ref27","article-title":"C3: Zero-shot text-to-SQL with ChatGPT","author":"Dong","year":"2023","journal-title":"arXiv:2307.07306"},{"key":"ref28","volume-title":"Generate SQL Queries in Seconds With AI","year":"2025"},{"key":"ref29","volume-title":"Convert Text To SQLWith AI, in Seconds","year":"2025"},{"issue":"4","key":"ref30","doi-asserted-by":"crossref","first-page":"2773","DOI":"10.3390\/ai5040134","article-title":"Trustworthy AI: Securing sensitive data in large language models","volume":"5","author":"Feretzakis","year":"2024","journal-title":"AI"},{"key":"ref31","article-title":"From prompt injections to SQL injection attacks: How protected is your LLM-integrated web application?","author":"Pedro","journal-title":"arXiv:2308.01990"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3649158.3657032"},{"key":"ref33","article-title":"GuardAgent: Safeguard LLM agents by a guard agent via knowledge-enabled reasoning","author":"Xiang","year":"2024","journal-title":"arXiv:2406.09187"},{"key":"ref34","article-title":"Large language models cannot self-correct reasoning yet","author":"Huang","year":"2023","journal-title":"arXiv:2310.01798"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1425"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-025-03662-6"},{"key":"ref37","article-title":"Evaluating LLMs for text-to-SQL generation with complex SQL workload","author":"Ma","year":"2024","journal-title":"arXiv:2407.19517"},{"key":"ref38","article-title":"Evaluating the data model robustness of text-to-SQL systems based on real user queries","author":"F\u00fcrst","year":"2024","journal-title":"arXiv:2402.08349"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.trustnlp-main.1"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-05043-z"},{"key":"ref41","volume-title":"Fine-grained Access Control in Amazon Opensearch Service","year":"2025"},{"issue":"1","key":"ref42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.54368\/qijirse.2.1.0007","article-title":"Cloud database: Empowering scalable and flexible data management","volume":"2","author":"Karunamurthy","year":"2023","journal-title":"Quing, Int. J. Innov. Res. Sci. Eng."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3764113"},{"issue":"1","key":"ref44","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TETC.2022.3193577","article-title":"Enabling attributebased access control in NoSQL databases","volume":"11","author":"Gupta","year":"2023","journal-title":"IEEE Trans. Emerg. Topics Comput."},{"key":"ref45","author":"Parte","year":"2025","journal-title":"Msaneme\/Text2SQL-Evaluation-Artifacts: Text-to-SQL Evaluation Artifacts (v1.0.0)"},{"key":"ref46","article-title":"DeepSeek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning","author":"Guo","year":"2025","journal-title":"arXiv:2501.12948"},{"key":"ref47","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"arXiv:2005.14165"},{"key":"ref48","volume-title":"GPT-4","year":"2025"},{"key":"ref49","volume-title":"GPT-4o Mini: Advancing Cost-Efficient Intelligence","year":"2025"},{"key":"ref50","volume-title":"Introducing OpenAI O1","year":"2024"},{"key":"ref51","volume-title":"OpenAI O3-Mini","year":"2025"},{"key":"ref52","volume-title":"SQLCoder","year":"2025"},{"key":"ref53","author":"Parte","year":"2025","journal-title":"Grys-UPM\/LLM-Based-HMI_evaluations: LLM-Enabled-HMI_llms-Experiments"},{"key":"ref54","volume-title":"PostgreSQL 17.4 Documentation","year":"2025"},{"key":"ref55","article-title":"Mixture-of-agents enhances large language model capabilities","volume":"abs\/2406.04692","author":"Wang","year":"2024","journal-title":"ArXiv"},{"key":"ref56","doi-asserted-by":"crossref","DOI":"10.1016\/j.nlp.2025.100140","article-title":"Comparative analysis of mixture-of-agents models for natural language inference with ANLI data","volume":"11","author":"Bavirthi","year":"2025","journal-title":"Natural Lang. Process. J."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11398081.pdf?arnumber=11398081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T20:50:34Z","timestamp":1772225434000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11398081\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3665980","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}