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LLM-SPSS: An Efficient LLM-Based Secure Partitioned Storage Scheme in Distributed Hybrid Clouds.

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Název: LLM-SPSS: An Efficient LLM-Based Secure Partitioned Storage Scheme in Distributed Hybrid Clouds.
Autoři: Zhou, Ran, Che, Bichen, Yang, Liangbin
Zdroj: Electronics (2079-9292); Jan2026, Vol. 15 Issue 1, p30, 18p
Témata: HYBRID cloud computing, CLOUD storage, LANGUAGE models, PARALLEL programming, CLASSIFICATION, PARALLEL algorithms, COMPUTER performance
Abstrakt: With the growing adoption of hybrid cloud storage, the identification and protection of sensitive information within large-scale unstructured data has become increasingly challenging. Traditional rule-based and machine learning approaches have limitations in context-aware sensitive data classification and large-scale processing. In this work, a novel framework named LLM-SPSS, implementing a secure and confidential storage layout for distributed hybrid clouds through a fine-tuned XLM-R Base model and multi-dimensional data partitioning, is proposed. First, a fine-tuned XLM-R Base model with adaptive prompt tuning is employed to enable context-aware sensitive data classification and improve detection accuracy. In addition, MapReduce-based distributed processing allows the framework to scale efficiently to large datasets, thus enhancing computational efficiency. Furthermore, a multi-dimensional cloud partitioning scheme provides secure and fine-grained storage isolation within hybrid cloud environments. Experimental results demonstrate that LLM-SPSS achieves an F1-score of 99.66% and yields a 6.3× speed-up over the non-distributed baseline, outperforming traditional rule-based (F1 68.27%), conventional machine learning (SVM F1 98.32%, Random Forest F1 95.79%), and other LLM-based approaches (DePrompt F1 95.95%) and effectively balancing high accuracy with computational efficiency. [ABSTRACT FROM AUTHOR]
Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: LLM-SPSS: An Efficient LLM-Based Secure Partitioned Storage Scheme in Distributed Hybrid Clouds.
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  Data: Electronics (2079-9292); Jan2026, Vol. 15 Issue 1, p30, 18p
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  Data: <searchLink fieldCode="DE" term="%22HYBRID+cloud+computing%22">HYBRID cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22CLOUD+storage%22">CLOUD storage</searchLink><br /><searchLink fieldCode="DE" term="%22LANGUAGE+models%22">LANGUAGE models</searchLink><br /><searchLink fieldCode="DE" term="%22PARALLEL+programming%22">PARALLEL programming</searchLink><br /><searchLink fieldCode="DE" term="%22CLASSIFICATION%22">CLASSIFICATION</searchLink><br /><searchLink fieldCode="DE" term="%22PARALLEL+algorithms%22">PARALLEL algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+performance%22">COMPUTER performance</searchLink>
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  Group: Ab
  Data: With the growing adoption of hybrid cloud storage, the identification and protection of sensitive information within large-scale unstructured data has become increasingly challenging. Traditional rule-based and machine learning approaches have limitations in context-aware sensitive data classification and large-scale processing. In this work, a novel framework named LLM-SPSS, implementing a secure and confidential storage layout for distributed hybrid clouds through a fine-tuned XLM-R Base model and multi-dimensional data partitioning, is proposed. First, a fine-tuned XLM-R Base model with adaptive prompt tuning is employed to enable context-aware sensitive data classification and improve detection accuracy. In addition, MapReduce-based distributed processing allows the framework to scale efficiently to large datasets, thus enhancing computational efficiency. Furthermore, a multi-dimensional cloud partitioning scheme provides secure and fine-grained storage isolation within hybrid cloud environments. Experimental results demonstrate that LLM-SPSS achieves an F1-score of 99.66% and yields a 6.3× speed-up over the non-distributed baseline, outperforming traditional rule-based (F1 68.27%), conventional machine learning (SVM F1 98.32%, Random Forest F1 95.79%), and other LLM-based approaches (DePrompt F1 95.95%) and effectively balancing high accuracy with computational efficiency. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/electronics15010030
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        Text: English
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      – SubjectFull: CLOUD storage
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      – SubjectFull: LANGUAGE models
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      – SubjectFull: PARALLEL programming
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      – SubjectFull: PARALLEL algorithms
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      – SubjectFull: COMPUTER performance
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      – TitleFull: LLM-SPSS: An Efficient LLM-Based Secure Partitioned Storage Scheme in Distributed Hybrid Clouds.
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              Text: Jan2026
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