60 lines
1.4 KiB
Plaintext
60 lines
1.4 KiB
Plaintext
---
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title: "Generating Secure Random Numbers in R"
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date: "`r Sys.Date()`"
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vignette: >
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteIndexEntry{Generating Secure Random Numbers in R}
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\usepackage[utf8]{inputenc}
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output:
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html_document
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---
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```{r, echo = FALSE, message = FALSE}
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knitr::opts_chunk$set(comment = "")
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library(openssl)
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```
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The `rand_bytes` function binds to [RAND_bytes](https://docs.openssl.org/1.1.1/man3/RAND_bytes/) in OpenSSL to generate cryptographically strong pseudo-random bytes. See the OpenSSL documentation for what this means.
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```{r}
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rnd <- rand_bytes(10)
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print(rnd)
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```
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Bytes are 8 bit and hence can have `2^8 = 256` possible values.
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```{r}
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as.numeric(rnd)
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```
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Each random byte can be decomposed into 8 random bits (booleans)
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```{r}
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x <- rand_bytes(1)
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as.logical(rawToBits(x))
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```
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## Secure Random Numbers
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`rand_num` is a simple (2 lines) wrapper to `rand_bytes` to generate random numbers (doubles) between 0 and 1.
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```{r}
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rand_num(10)
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```
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To map random draws from [0,1] into a probability density, we can use a [Cumulative Distribution Function](https://en.wikipedia.org/wiki/Cumulative_distribution_function). For example we can combine `qnorm` and `rand_num` to simulate `rnorm`:
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```{r}
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# Secure rnorm
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x <- qnorm(rand_num(1000), mean = 100, sd = 15)
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hist(x)
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```
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Same for discrete distributions:
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```{r}
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# Secure rbinom
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y <- qbinom(rand_num(1000), size = 20, prob = 0.1)
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hist(y, breaks = -.5:(max(y)+1))
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```
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