Problem
Research questions and friction points this paper is trying to address.
guesswork
side-information
tail probabilities
i.i.d. random sequence
conditional tilted distributions
Tail exponents of conditional guesswork via the method of types
🤖 AI Summary
研究了基于相关侧信息猜测独立同分布随机序列的问题，通过类型计数法估计无侧信息情况下的猜测次数尾概率，并扩展到有条件情况，应用到密码猜测。
📝 Abstract
We study the problem of guessing a realization of an i.i.d. random sequence given element-wise correlated side-information. We use type-counting to provide estimates of the tail probabilities of the number of guesses for the case without side-information, which was shown earlier through large-deviation techniques. We then extend the same counting argument to the conditional setting, obtaining new explicit expressions for the corresponding guesswork exponents as divergences involving conditional tilted distributions. Finally, we provide an application of these exponents to brute-force password guessing with side-information.
Innovation
Methods, ideas, or system contributions that make the work stand out.
type-counting
conditional guesswork
divergences
conditional tilted distributions
brute-force password guessing
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