A distortion based approach for protecting inferences
A distortion based approach for protecting inferences
复制标题
一种基于失真的保护推论的方法
DOI：
10.1109/isit.2017.8006862
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
S. Diggavi
中科院分区：
文献类型：
--
作者：
Chi;G. Agarwal;C. Fragouli;S. Diggavi
推理系统中的窃听攻击不是为了学习原始数据，而是为了学习系统的推理来预测和操纵系统的行为。我们认为，传统的信息安全措施在对手的估计能力上可能是模糊的，而采用基于扭曲的框架，使其能够在度量空间上操作。我们证明，即使对于块长度为1的代码，要求完全基于扭曲的安全性比要求完美的信息论保密更节省，在某些情况下提供无界增益。在此框架内，我们设计了能够有效利用共享随机性的算法，并证明了共享随机密钥的每一位都具有指数级的安全性。
Eavesdropping attacks in inference systems aim to learn not the raw data, but the system inferences to predict and manipulate system actions. We argue that conventional information security measures can be ambiguous on the adversary's estimation abilities, and adopt instead a distortion based framework that enables to operate over a metric space. We show that requiring perfect distortion-based security is more frugal than requiring perfect information-theoretic secrecy even for block length one codes, offering in some cases unbounded gains. Within this framework, we design algorithms that enable to efficiently use shared randomness, and show that each bit of shared random key is exponentially useful in security.