摘要
大数据时代下,计算机隐私信息安全面临严峻挑战,数据全生命周期风险加剧、攻击手段智能化及多技术融合催生的复合型风险尤为突出。文章聚焦技术防护维度,剖析隐私威胁特征,重点阐述联邦学习、安全多方计算等创新技术的实践应用(如医疗联盟跨机构协作、金融信贷安全共享),并通过实验对比差分隐私、同态加密等技术的隐私保护强度与计算效率,验证技术方案在平衡隐私安全与数据效用中的有效性。研究为构建以技术为核心、以法律规范与管理机制为辅的隐私防护体系提供实践支撑,并助力解决大数据环境下隐私保护与数据利用的矛盾。
In the era of big data,computer privacy and information security are facing severe challenges,with increased risks throughout the data lifecycle,intelligent attack methods,and composite risks arising from the integration of multiple technologies.The article focuses on the dimension of technical protection,analyzes the characteristics of privacy threats,and focuses on the practical application of innovative technologies such as federated learning and secure multi-party computing(such as cross institutional collaboration in medical alliances and secure sharing of financial credit).Through experiments,the privacy protection strength and computational efficiency of technologies such as differential privacy and homomorphic encryption are compared to verify the effectiveness of the technical solutions in balancing privacy security and data utility.The research provides practical support for building a privacy protection system centered on technology and supplemented by legal norms and management mechanisms,helping to solve the contradiction between privacy protection and data utilization in the big data environment.
作者
余克南
YU Kenan(Guangdong Province Nanfang Technician College,Shaoguan Guangdong 512000,China)
出处
《信息与电脑》
2025年第15期87-89,共3页
Information & Computer
关键词
大数据
计算机隐私信息
安全研究
big data
computer privacy information
security research
作者简介
余克南,男,本科。研究方向:计算机硬件、软件应用。
