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基于差分隐私定义的社会化推荐效用模型

发表时间:2016-11-30  浏览量:1913  下载量:407
全部作者: 陈亮,杜秀春,朱培栋
作者单位: 国防科学技术大学计算机学院
摘 要: 基于差分隐私定义,提出一种社会化推荐效用的度量方法。该方法定义了效用函数的可交换性与集中性特点,通过计算目标推荐节点的共有邻居节点数来分析净化网络的隐私边界,给出了社会化推荐隐私保护与社会化推荐效用之间的限制条件。
关 键 词: 计算机应用;社会化推荐;效用函数;隐私保护;差分隐私
Title: Social recommendation utility model based on differential privacy
Author: CHEN Liang, DU Xiuchun, ZHU Peidong
Organization: College of Computer, National University of Defense Technology
Abstract: This paper, we propose a social recommendation utility measurement based on the differential privacy, which defines the characteristics of interchangeability and centralized for utility function. By calculating the total numbers of nodes in the network of the recommended target neighbor nodes, we analyze purify privacy boundaries to give limitation conditions of social recommendation and privacy preserving.
Key words: computer applications; social recommendation; utility function; privacy preserving; differential privacy
发表期数: 2016年11月第22期
引用格式: 陈亮,杜秀春,朱培栋. 基于差分隐私定义的社会化推荐效用模型[J]. 中国科技论文在线精品论文,2016,9(22):2290-2295.
 
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