The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of techniques, such as machine learning and deep learning traffic classifiers, have been applied to analyze users app traffic. These techniques allow the monitor to get the fingerprints of using apps while the user traffic is still encrypted, which raises a severe privacy issue. In order to fight against this type of data analysis, people have been researching obfuscation algorithms to confuse feature-based machine learning classifiers with data camouflage by modification on packet length distribution. The existing works achieve this goal by remapping traffic packet length distribution from the source app to the fake camouflage app. However, this solution suffers from its lack of scalability and flexibility in practical application since the method needs to pre-sample the target fake apps traffic before the use of traffic camouflage. In this paper, we proposed a practical solution by using a mathematical model to calculate the target distribution while maintaining at least 50 percent accuracy drops on the performance of the AppScanner mobile traffic classifier and roughly 20 percent overhead created during packet modification.

翻译：移动应用程序的使用在这个数字时代是无法避免的。 虽然每天生成大量数据, 用户隐私安全关注成为一个重要的问题。 如今, 大量技术, 如机器学习和深学习交通分类器, 已经应用来分析用户应用程序流量。 这些技术允许监视器在用户流量仍然加密的情况下获得应用程序使用的指纹, 这引起了严重的隐私问题。 为了打击这种类型的数据分析, 人们一直在研究模糊的算法, 通过修改包装长度分布, 将基于特性的机器学习分类器与数据伪装混淆起来。 现有的工程通过将源应用程序的交通包长度分布重新绘制成假伪装应用程序来实现这一目标。 但是, 由于在使用交通迷彩之前, 需要预先标注假应用程序流量的方法, 因而这种解决办法缺乏可缩放性和实际应用的灵活性。 在本文中, 我们提出了一个切实可行的解决方案, 使用数学模型来计算目标分布, 同时保持至少50%的精确度, 用于AppScanner移动交通分类器的性能, 和在包装修改过程中创造的大约20% 的间接费用。