面向大数据通信的隐私保护与加密算法研究
摘 要
随着大数据时代的到来，数据通信量呈指数级增长，隐私保护成为亟待解决的关键问题。本研究聚焦于大数据通信环境下的隐私保护与加密算法设计，旨在构建高效、安全的数据传输体系。通过分析现有加密技术的局限性，提出了一种基于属性基加密（ABE）和同态加密（HE）融合的新型加密框架，该框架能够在保证数据可用性的前提下实现细粒度访问控制。实验结果表明，所提出的方案在密钥生成效率、加解密速度及存储开销等方面均优于传统方法。特别是在大规模数据集测试中，新算法展现出优异的性能表现，能够有效抵御各类攻击并确保数据完整性。本研究的主要创新点在于将属性基加密与同态加密有机结合，解决了现有技术中存在的计算复杂度高、密钥管理困难等问题，为大数据通信提供了更加完善的安全保障机制，对推动隐私保护技术的发展具有重要意义。
关键词：隐私保护；属性基加密；同态加密
Abstract
With the advent of the big data era, the volume of data communication has grown exponentially, making privacy protection a critical issue that urgently needs to be addressed. This study focuses on privacy protection and encryption algorithm design in big data communication environments, aiming to establish an efficient and secure data transmission system. By analyzing the limitations of existing encryption technologies, this research proposes a novel encryption fr amework that integrates Attribute-Based Encryption (ABE) with Homomorphic Encryption (HE). This fr amework enables fine-grained access control while ensuring data usability. Experimental results demonstrate that the proposed scheme outperforms traditional methods in terms of key generation efficiency, encryption and decryption speed, and storage overhead. Notably, in large-scale dataset tests, the new algorithm exhibits superior performance, effectively resisting various attacks and ensuring data integrity. The primary innovation of this study lies in the organic combination of ABE and HE, addressing issues such as high computational complexity and difficult key management present in existing technologies. This provides a more comprehensive security mechanism for big data communication and holds significant implications for advancing privacy protection technologies.
Keywords: Privacy Protection；Attribute-Based Encryption；Homomorphic Encryption
目 录
摘 要 I
Abstract II
引言 1
一、大数据通信隐私威胁分析 1
（一）隐私泄露风险评估 1
（二）数据传输安全挑战 2
（三）用户信息保护需求 2
二、加密算法基础研究 2
（一）对称加密技术应用 2
（二）非对称加密机制 3
（三）哈希函数的作用 3
三、隐私保护技术创新 4
（一）差分隐私理论探索 4
（二）同态加密的应用 4
（三）安全多方计算方法 5
四、实施与优化策略 5
（一）算法性能优化方案 5
（二）隐私保护系统设计 6
（三）实际应用案例分析 6
结 论 7
致 谢 8
参考文献 9
摘 要
随着大数据时代的到来，数据通信量呈指数级增长，隐私保护成为亟待解决的关键问题。本研究聚焦于大数据通信环境下的隐私保护与加密算法设计，旨在构建高效、安全的数据传输体系。通过分析现有加密技术的局限性，提出了一种基于属性基加密（ABE）和同态加密（HE）融合的新型加密框架，该框架能够在保证数据可用性的前提下实现细粒度访问控制。实验结果表明，所提出的方案在密钥生成效率、加解密速度及存储开销等方面均优于传统方法。特别是在大规模数据集测试中，新算法展现出优异的性能表现，能够有效抵御各类攻击并确保数据完整性。本研究的主要创新点在于将属性基加密与同态加密有机结合，解决了现有技术中存在的计算复杂度高、密钥管理困难等问题，为大数据通信提供了更加完善的安全保障机制，对推动隐私保护技术的发展具有重要意义。
关键词：隐私保护；属性基加密；同态加密
Abstract
With the advent of the big data era, the volume of data communication has grown exponentially, making privacy protection a critical issue that urgently needs to be addressed. This study focuses on privacy protection and encryption algorithm design in big data communication environments, aiming to establish an efficient and secure data transmission system. By analyzing the limitations of existing encryption technologies, this research proposes a novel encryption fr amework that integrates Attribute-Based Encryption (ABE) with Homomorphic Encryption (HE). This fr amework enables fine-grained access control while ensuring data usability. Experimental results demonstrate that the proposed scheme outperforms traditional methods in terms of key generation efficiency, encryption and decryption speed, and storage overhead. Notably, in large-scale dataset tests, the new algorithm exhibits superior performance, effectively resisting various attacks and ensuring data integrity. The primary innovation of this study lies in the organic combination of ABE and HE, addressing issues such as high computational complexity and difficult key management present in existing technologies. This provides a more comprehensive security mechanism for big data communication and holds significant implications for advancing privacy protection technologies.
Keywords: Privacy Protection；Attribute-Based Encryption；Homomorphic Encryption
目 录
摘 要 I
Abstract II
引言 1
一、大数据通信隐私威胁分析 1
（一）隐私泄露风险评估 1
（二）数据传输安全挑战 2
（三）用户信息保护需求 2
二、加密算法基础研究 2
（一）对称加密技术应用 2
（二）非对称加密机制 3
（三）哈希函数的作用 3
三、隐私保护技术创新 4
（一）差分隐私理论探索 4
（二）同态加密的应用 4
（三）安全多方计算方法 5
四、实施与优化策略 5
（一）算法性能优化方案 5
（二）隐私保护系统设计 6
（三）实际应用案例分析 6
结 论 7
致 谢 8
参考文献 9
