使用约束填充优化隐藏对象大小
Optimally Hiding Object Sizes with Constrained Padding
摘要：
最具挑战性的流量分析攻击是那些利用网络上下载的对象大小的攻击。在本文中，我们系统地分析了这个问题的现实约束下，填充开销的对象存储愿意承担。我们给出了算法来计算隐私最佳填充方案-特别是最小化网络观察者从下载对象的填充大小中获得的信息增益-在几种感兴趣的情况下：每对象填充，其中对象存储响应每个对具有相同填充副本的对象的请求;每请求填充，其中对象存储每次为该对象提供服务时都重新填充该对象;以及与前面的场景不同的场景，其中对象存储不能在对象查询上利用已知的分布。我们在每种情况下提供隐私最优填充的构造，将它们与研究文献中的最新竞争者进行比较，并评估它们在实际数据集上的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
