数字营销计划的设计基于异常的消费者行为数据分类和改进的同型加密算法
Jun Cui1, Hao Jiang2, Zhendan Xu3
1Business School, Hohai University, Nanjing, Jiangsu, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了一种使用消费者数据和同态加密的新型数字营销框架. 改进后的模型在分类在线消费数据方面实现了高准确性,改善了市场洞察力和营销策略.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络安全 网络安全
背景情况:
- 数字营销严重依赖消费者数据,引发了隐私问题.
- 现有的加密方法可能无法有效处理复杂的消费者数据集.
- 同型加密为保护隐私的数据分析提供了一个有前途的解决方案.
研究的目的:
- 开发一个保护隐私的数字营销框架,使用同型加密和消费者数据.
- 增强对消费数据中的理数处理的同态加密.
- 提高在线消费数据分类的准确性和效率.
主要方法:
- 使用GridSearch与CatBoost模型来处理叶节点.
- 采用一个辐射基函数 (RBF) 层来映射叶节点.
- 集成了一个增强的同态加密算法与中国残余定理进行解密.
主要成果:
- 实现曲线下的面积 (AUC) 为0.66.
- 在线消费数据的分类准确率达到98.56%.
- 获得了98.41的F1得分,证明了强大的模型概括性.
结论:
- 拟议的框架为市场洞察提供了稳定,安全和高效的解决方案.
- 增强的同态加密在处理理数时有效地保护隐私.
- 该模型允许精确,实时的市场分析,以优化数字营销策略.
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