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BIT-TRAPS: Building Information-Theoretic Traffic Privacy Into Packet Streams

机译:BIT-TRAPS:将信息理论的流量隐私构建到数据包流中

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Sniffing encrypted data packets traveling across networks can often be useful in inferring nontrivial information about their contents because of the manner in which the transmission of such packets is handled by lower layers in the communications protocol stack. In this paper, we formally study the side-channel formed by variable packet sizes, and explore obfuscation approaches to prevent information leakage while jointly considering the practical cost of obfuscation. We show that randomized algorithms for obfuscation perform best and can be studied as well-known information-theoretic constructs, such as discrete channels with and without memory. We envision a separate layer called a ${ssr Bit-Trap}$ , that employs buffering and bit-padding as orthogonal methods for obfuscating such side channels. For streams of packets, we introduce the use of mutual-information rate as an appropriate metric for the level of obfuscation that captures nonlinear relationships between original and modified streams. Using buffering-delay and average bit-padding as the respective costs, a ${ssr Bit-Trap}$ formulates a constrained optimization problem with bounds on the average costs, to implement the best possible obfuscation policy. We find that combining small amounts of delay and padding together can create much more obfuscation than either approach alone, and that a simple convex trade-off exists between buffering delay and padding for a given level of obfuscation.
机译:嗅探经过网络传播的加密数据包通常可用于推断有关其内容的重要信息,因为通信协议堆栈中的较低层处理此类数据包的传输方式。在本文中,我们正式研究了由可变数据包大小形成的边信道,并在结合考虑混淆的实际成本的同时探索了防止信息泄漏的混淆方法。我们表明,模糊处理的随机算法性能最好,可以作为众所周知的信息理论构造进行研究,例如带有和不带有内存的离散通道。我们设想了一个称为$ {ssr Bit-Trap} $的单独层,该层使用缓冲和位填充作为正交方法来混淆此类边信道。对于数据包流,我们引入互信息率作为混淆级别的一种适当度量,以混淆级别捕获原始流与修改后流之间的非线性关系。 $ {ssr Bit-Trap} $使用缓冲延迟和平均位填充作为各自的成本,制定了约束平均平均成本范围的优化问题,以实现最佳的混淆策略。我们发现,将少量延迟和填充结合在一起比单独使用任何一种方法都可以产生更多的混淆,并且对于给定的混淆水平,在缓冲延迟和填充之间存在简单的凸折衷。

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