Earticle

퓨시아(Fuchsia)는 구글(Google)이 데스크탑에서부터 IoT 기기까지의 환경에서 사용될 수 있도록 공개적으로 개발하고 있는 운영체제이다. 이는 구글이 개발하고 있는 지르콘(Zircon)이라는 마이크로 커널(microkernel)을 기반으로 한다. 암호키, 보안 매개변수 등을 생성하는 데 사용되는 난수발생기의 취약성은 운영체제의 안전성에 영향을 미치므로, 퓨시아 및 지르콘에서 사용하는 난수발생기에 대한 안전성 분석이 필요하다. 본 논문에서는 퓨시아 및 지르콘의 난수발생기에 대한 구조와 사용하는 엔트로피 소스를 분석한다. 또한, 퓨시아 및 지르콘의 난수발생기의 유일한 입력이 될 가능성이 큰 지터 엔트로피 소스에 대한 엔트로피를 추정하고, 지터 엔트로피 소스에 대한 전수조사 방법과 전수조사량을 제시한다. 본 논문의 결과는 다양한 환경에서 동작하는 퓨시아 및 지르콘 난수발생기에 대한 공격 가능성을 보여주며, 지터 엔트로피 소스를 사용하는 여러 운영체제의 난수발생기에 대한 안전성을 분석하는 데 활용될 것으로 기대한다.

Fuchsia is an operating system that is being developed publicly by Google to be used in environments ranging from desktops to IoT devices. It is based on a microkernel called Zircon that Google is developing. Since the vulnerability of the random number generator(RNG) used to generate cryptographic keys and security parameters affects the security of the operating system, it is necessary to analyze the security of the RNG used in Fuchsia and Zircon. In this paper, we analyze the structure of the RNG used in Fuchsia and Zircon and the entropy source used by RNG. In addition, we estimate the entropy of the jitter entropy source, which is likely to be the only input of the RNG of Fuchsia and Zircon, and propose a method and a cost of exhaustive search for the jitter entropy source. The results of this paper show the possibility of attacking RNG of the Fuchsia and Zircon operating in various environments and are expected to be used to analyze the security of RNGs of various operating systems that use the jitter entropy source.

We establish a new model of privacy-preserving one-class support vector machine (SVM) based on vertically partitioned data. Every participant holds all the data with a part of attributes. They apply different random matrices to establish their own kernel matrix. By sharing these partial kernel matrices, we construct a global kernel matrix and establish linear and nonlinear privacy-preserving models. Experimental results on benchmark data sets verify the validity of the proposed models.

Random elements in $L^1(R)$ and some properties of $L^1(R)$ space are investigated with application to kernel density estimators. A weak law of large numbers for compact uniformly integrable random elements is introduced for further application.

In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if $Y{\geq}T$, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to $n^{-1/6}$ modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.