Glossary
- min-entropy
The min-entropy, \(k\), of a random variable, \(X \in \{ 0,1\}^n\), is defined as
\[k = H_{\infty}(X) = -\log _{ \max x \in \{ 0,1\}^n} \Pr (X = x | \lambda)\]where \(\lambda\) is any additional side information a physical observer may have. This can be interpreted as the minimum amount of random bits a random variable \(X\) has, conditioned on the side information \(\lambda\).
- block min-entropy
A set of random variables \(X_i\) for \(i \in \mathbb{Z}\) is said to have block min entropy \(k\), if
\[k = H_{\infty}(X_i) = -\log _{ \max x \in \{ 0,1\}^n} \Pr (X_i = x | X_0, X_1, ..., X_{i-1}, \lambda)\]for all \(i\), where \(\lambda\) is any additional side information a physical observer may have. This can be interpreted as the minimum amount of random bits a random variable \(X_i\) has, even when conditioned on all previous random variables in the set and side information \(\lambda\).
- statistical distance
The statistical distance, \(\Delta\), between two random variables, \(X\) and \(Z\) \(\in \{0,1\}^n\) is defined as
\[\Delta(X,Z) = \frac{1}{2} \sum_{v \in \{ 0,1 \}^n} | \Pr(X=v | \lambda) - \Pr(Z=v | \lambda)|\]where \(\lambda\) is any additional side information a physical observer may have. This is a measure of how close, or indistinguishable, two distributions are to one another.
- perfect randomness
A distribution \(X\) on \(\{0,1\}^n\) is said to be perfectly random, if,
\[\Delta(X, U_n) = 0\]where \(U_n\) is the uniform variable on \(\{0,1\}^n\). This definition is equivalent to saying that the variable \(X\) is completely indistinguishable from the uniform distribution to a physical observer. Note: This is a composable definition, any random variable \(X\) satisfying this definition can be safely used in practical applications.
- near-perfect randomness
A random variable \(X\) on \(\{0,1\}^n\) is said to be near-perfectly random, if,
\[\Delta(X, U_n) \leq \epsilon\]where \(U_n\) is the uniform variable on \(\{0,1\}^n\). This definition is equivalent to saying that the variable \(X\) is \(\epsilon\) close to indistinguishable from the uniform distribution to a physical observer. Note: This is a composable definition, any random variable \(X\) satisfying this definition can be safely used in practical applications.
- deterministic randomness extractor
A \((k, \epsilon, n, m)\)-deterministic randomness extractor is a function
\[\mathrm{Ext}_d: \{ 0,1\}^n \rightarrow \{0,1\}^m\]such that, for every random variable \(X\) on \(\{ 0,1\}^n\) with min-entropy \(H_{\infty}(X) \geq k\), then,
\[\Delta(\mathrm{Ext}_d(X), U_m) \leq \epsilon\]where \(U_m\) is the uniform variable on \(\{0,1\}^m\). In words, a deterministic extractor is a deterministic function that maps a random variable \(X\) to a new variable \(\mathrm{Ext}_d(X)\) that is near-perfect, as defined in near-perfect randomness.
- seeded randomness extractor
A \((k, \epsilon, n, d, m)\)-seeded randomness extractor is a function
\[\mathrm{Ext}_s: \{ 0,1\}^{n} \times \{0,1\}^{d} \rightarrow \{0,1\}^m\]such that, for every random variable \(X\) on \(\{ 0,1\}^{n}\) with min-entropy \(H_{\infty}(X) \geq k\), and every \(S\) on \(\{ 0,1\}^d\) with min-entropy \(H_{\infty}(Y) = d\) then,
\[\Delta(\mathrm{Ext}_s(X, S), U_m) \leq \epsilon\]where \(U_m\) is the uniform distribution on \(\{0,1\}^m\). In words, a seeded extractor is a randomized function that maps a random variable \(X\) to a new variable \(\mathrm{Ext}_s(X, S)\) that is near-perfect, as defined in near-perfect randomness.
- 2-source randomness extractor
A \((k_1, k_2, \epsilon, n_1, n_2, m)\)-2-weak-source randomness extractor is a function
\[\mathrm{Ext}_2: \{ 0,1\}^{n_1} \times \{0,1\}^{n_2} \rightarrow \{0,1\}^m\]such that, for every independent random variable \(X\) on \(\{ 0,1\}^{n_1}\) with min-entropy \(H_{\infty}(X) \geq k_1\), and \(Y\) on \(\{ 0,1\}^{n_2}\) with min-entropy \(H_{\infty}(Y) \geq k_2\) then,
\[\Delta(\mathrm{Ext}_2(X, Y), U_m) \leq \epsilon\]where \(U_m\) is the uniform variable on \(\{0,1\}^m\). In words, a 2-weak-source extractor is a weakly randomized function that maps a random variable \(X\) to a new variable \(\mathrm{Ext}_2(X, Y)\) that is near-perfect, as defined in near-perfect randomness.
- strong seeded extractor
A strong seeded randomness extractor is any \(\mathrm{Ext}_s\) s.t.
\[\Delta( (\mathbf{Ext}_s(X, S), S), (U_m, S) ) \leq \epsilon\]where \(U_m\) is the uniform variable on \(\{0,1\}^m\). Note, we use bold font to denote strong extractors. In words, a strong seeded extractor is a randomized function that maps a random variable \(X\) to a new variable \(\mathbf{Ext}_s(X, S)\) that is near-perfect, as defined in near-perfect randomness and where \(\mathrm{Ext}_s(X, S)\) is (near-) independent of \(X\).
Intuitively, this has 3 main implications:
The seed \(S\) can be made public without compromising the uniformity of the extractor output.
The seed \(S\) can be concatenated with the output \(\mathbf{Ext_s}(X,S)\) to get a longer, (near-)perfect output.
The seed \(S\) can be re-used with different input sources.