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QoS-based web service prediction is widely used in service computing but raises privacy concerns when collecting user data. Although local differential privacy (LDP) offers strong protection, its direct use in QoS prediction is hindered by attribute heterogeneity, which reduces data utility and prediction accuracy. To address this, we propose LDP-QWSP, an LDP-compliant framework that incorporates attribute-aware normalization and a perturbation mechanism favoring high-utility intervals. Experiments on real-world datasets confirm that LDP-QWSP achieves higher prediction accuracy than existing methods under comparable privacy budgets.

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LDP-QWSP: A General Local Differential Privacy Framework for QoS-Based Web Service Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Fuchang Luo,
  • Haonan Wu,
  • Shunshun Peng,
  • Quanwang Wu,
  • Hongbing Wang,
  • Mengmeng Yang,
  • Taolin Guo

摘要

QoS-based web service prediction is widely used in service computing but raises privacy concerns when collecting user data. Although local differential privacy (LDP) offers strong protection, its direct use in QoS prediction is hindered by attribute heterogeneity, which reduces data utility and prediction accuracy. To address this, we propose LDP-QWSP, an LDP-compliant framework that incorporates attribute-aware normalization and a perturbation mechanism favoring high-utility intervals. Experiments on real-world datasets confirm that LDP-QWSP achieves higher prediction accuracy than existing methods under comparable privacy budgets.