| 一种基于差分隐私的任务分配方案 |
投稿时间：2026-03-18 修订日期：2026-05-11  点此下载全文 |
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| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| 中文摘要:为保护众包任务分配中双方的位置隐私，本文提出基于差分隐私的任务分配方案TABGD。该方案利用网格范围计数替代精确查询，避免平台不可信问题，同时保证高效率和低开销。TABGD包含两个核心算法：自适应阈值动态网格分解算法（ATDGD）通过三级网格的分割与合并及Laplace噪声，实现任务与工作者位置的双边隐私保护；基于索引树的贪心算法（GABI）使用格雷码索引优化多播区域构建，平衡任务接受率与系统开销。实验表明，隐私预算?=1.0时，ATDGD数据可用性达87%以上，较现有方法提升6.73%，且无位置丢失；GABI的任务接受成功率超过86%，系统开销降低31.6%。 |
| 中文关键词:空间众包 差分隐私 网格划分 任务分配 |
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| A Task Allocation Scheme Based on Differential Privacy |
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| Abstract:To protect bilateral location privacy in crowdsourcing task allocation, this paper proposes a differential privacy-based scheme named TABGD. It replaces precise queries with grid range counts to address platform untrustworthiness while ensuring high efficiency and low overhead. TABGD includes two core algorithms: ATDGD, which uses three-level grid splitting/merging with Laplace noise for bilateral privacy protection, and GABI, which employs Gray-code indexing to optimize multicast region construction, balancing task acceptance rate and system cost. Experiments show that with privacy budget ?=1.0, ATDGD achieves over 87% data utility (6.73% improvement) with no location loss, and GABI attains over 86% task acceptance while reducing system overhead by 31.6%. |
| keywords:Spatial Crowdsourcing Differential privacy Spatial grid partitioning Task Allocation |
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