政务云环境下的数据共享隐私保护增强方案  

Enhanced privacy protection scheme for data sharing in government cloud environment

作　　者：沈博 杨军 王福喜[1] 杨菲 徐勇勇 SHEN Bo;YANG Jun;WANG Fuxi;YANG Fei;XU Yongyong(The 15th Research Institute,China Electronics Technology Group Corporation,Beijing 100083,China)

机构地区：[1]中国电子科技集团公司第十五研究所,北京100083

出　　处：《沈阳师范大学学报(自然科学版)》2025年第1期28-36,共9页Journal of Shenyang Normal University:Natural Science Edition

基　　金：国家自然科学基金资助项目(U20B2044)。

摘　　要：针对云环境下隐私数据的安全防护力度不足和无法有效释放数据价值的问题,提出一种基于差分隐私和属性加密的数据共享隐私保护增强方案。该方案首先使用差分隐私技术为预处理后的数据敏感部分添加噪声,降低上云数据隐私泄露风险,确保共享数据的可用性。同时,采用改进基于属性的代理重加密算法保护隐私化数据,提供恒定长度密文,以减少加解密计算时间和通信开销,提升数据在传输过程中的机密性,实现政务云环境下隐私数据的安全传输、存储与应用。理论分析证明,方案在判定双线性Diffie-Hellman(DBDH)问题下是安全的。在Adult、Iris等数据集上的实验结果表明,该方案可有效兼顾隐私性和可用性,所产生的隐私化数据具有较好的分类准确率。A data-sharing privacy protection enhancement scheme,incorporating differential privacy and attribute-based encryption,is proposed to address the challenges of inadequate security and inefficient data value utilization in cloud environments.The scheme initially applies differential privacy techniques to introduce noise into sensitive parts of preprocessed data,thereby reducing the privacy leakage risk while ensuring shared data availability.Simultaneously,an improved attribute-based proxy re-encryption algorithm is implemented to secure private data.This approach produces constant-length ciphertext,reducing encryption and decryption computation time and minimizing communication overhead.It enhances data confidentiality during transmission,ensuring secure data transmission,storage,and application within cloud environments.Theoretical analysis confirms the scheme s security under the Decisiond Bilinear Diffie-Hellman(DBDH)assumption.Experimental results on four datasets,including Adult and Iris,demonstrate that the scheme effectively balances privacy and usability,maintaining high classification accuracy in the generated privacy-preserving data.

关 键 词：云环境 隐私保护 数据共享 差分隐私 属性加密 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]