基于ai URL分类的组洋葱路由差分隐私保护

I. Liu, Yung-Lin Chang, Jung-Shian Li, Chuan-Gang Liu
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引用次数: 2

摘要

随着平板电脑、智能手机等移动信息设备的迅速普及,无线通信技术得到了充分的发展和广泛的部署。移动互联网接入已经成为人们日常与世界交流的主要和重要方式,其隐私保护也受到人们的关注。洋葱路由器,更广为人知的名字是Tor,是一种不受区域隐私保护限制的匿名互联网通信技术。除此之外,Tor还可以到达普通搜索引擎无法搜索到的网站。根据Tor的观点,传输的数据在到达服务器之前就像洋葱一样被层层加密。本研究提出了一种利用机器学习技术在访问前预测URL类别的系统。根据类别预测,我们在洋葱路由上用三种RSA密钥长度表示不同的隐私级别。根据不同的情况,我们的系统可以在安全性和时间成本之间取得平衡。因此,我们提出的方案可以使洋葱路由更加灵活和高效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differential Privacy Protection with Group Onion Routing based on AI-based URL Classification
Due to the rapid spread of tablet computers, smartphones, and other mobile information devices, wireless communication technology is fully developed and deployed widely. Mobile Internet access has been a main and important way to communicate the world in daily and it privacy protection also catches much attentions. The Onion Router, better known as Tor, is a technique for anonymous communication over internet without regional restrictions for privacy protection. Apart from this, Tor can reach sites that normal search engine cannot search. As the opinion of Tor, the transmitted data has been encrypted layer by layer, just like onion, before it reaching server. Our research proposed a system predicting URL's category with the use of machine learning technique before visiting. According to the category prediction, we represent different privacy level with three kinds of RSA key lengths on onion routing. Depending on various situations, our system can obtain the balance between security and time cost. Hence, our proposed scheme can make onion routing more flexible and efficiently.
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