Among the most challenging traffic-analysis attacks to confound are those leveraging the sizes of objects downloaded over the network. In this paper we systematically analyze this problem under realistic constraints regarding the padding overhead that the object store is willing to incur. We give algorithms to compute privacy-optimal padding schemes -- specifically that minimize the network observer's information gain from a downloaded object's padded size -- in several scenarios of interest: per-object padding, in which the object store responds to each request for an object with the same padded copy; per-request padding, in which the object store pads an object anew each time it serves that object; and a scenario unlike the previous ones in that the object store is unable to leverage a known distribution over the object queries. We provide constructions for privacy-optimal padding in each case, compare them to recent contenders in the research literature, and evaluate their performance on practical datasets.

翻译：最具有挑战性的交通分析攻击令人困惑的是利用从网络下载的物体的大小。在本文中,我们系统分析这个问题,在物体存储处愿意承担的垫面设计上的现实限制下。我们给出算法来计算隐私最佳垫面方案,特别是最大限度地减少网络观察者从下载物体的垫面设计中获得的信息 -- -- 在几种感兴趣的情景中:单项垫面设计,其中对象存储处对每个物体的请求都作出回应,同时附上相同的印本;单项垫面设计,其中物体存储处每次服务该物体时都重新贴上物体;以及与先前的情况不同,物体存储处无法对物体查询进行已知的分布。我们为每个案例的隐私最佳垫面设计图案,将它们与研究文献中的最新竞争者进行比较,并评价其在实用数据集上的性能。