منابع مشابه
Constant-time Discrete Gaussian Sampling
Sampling from a discrete Gaussian distribution is an indispensable part of lattice-based cryptography. Several recent works have shown that the timing leakage from a non-constant-time implementation of the discrete Gaussian sampling algorithm could be exploited to recover the secret. In this paper, we propose a constant-time implementation of the Knuth-Yao random walk algorithm for performing c...
متن کاملGaussian Sampling over the Integers: Efficient, Generic, Constant-Time
Sampling integers with Gaussian distribution is a fundamental problem that arises in almost every application of lattice cryptography, and it can be both time consuming and challenging to implement. Most previous work has focused on the optimization and implementation of integer Gaussian sampling in the context of specific applications, with fixed sets of parameters. We present new algorithms f...
متن کاملHigh Precision Discrete Gaussian Sampling on FPGAs
Lattice-based public key cryptography often requires sampling from discrete Gaussian distributions. In this paper we present an efficient hardware implementation of a discrete Gaussian sampler with high precision and large tail-bound based on the Knuth-Yao algorithm. The Knuth-Yao algorithm is chosen since it requires a minimal number of random bits and is well suited for high precision samplin...
متن کاملDiscrete-Time Randomized Sampling
Techniques for developing low-complexity, robust Digital Signal Processing (DSP) algorithms with low-power consumption have become increasingly important. This thesis explores a framework, discrete-time randomized sampling, which allows the design of algorithms that meet some desired complexity, robustness or power constraints. Three distinct sampling schemes are presented based on randomized s...
متن کاملConstant-Time Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well calibrated posterior distributions. Recent advances in inducing point methods have drastically sped up marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address this shortcoming...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Computers
سال: 2018
ISSN: 0018-9340,1557-9956,2326-3814
DOI: 10.1109/tc.2018.2814587