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An almost strongly universal hash family relaxes the exact ordered pair balance of a strongly universal hash family. In the convention used here, choose $h$ with the uniform distribution on a finite set from a family with $q$ possible outputs. Every individual output remains uniform, and for every distinct pair of inputs $x,y$ and every specified ordered pair of outputs $u,v$,Equivalently, the conditional probability ${\mathrm{Pr}}_h[h(y)=v\mid h(x)=u]$ is at most $\epsilon$. This asks how predictable a second output can become after the first is known.

For $q=5$, strongly universal hash families give $\epsilon =1/5$: learning one output leaves each specified second output with conditional probability $1/5$. A looser guarantee $\epsilon =2/5$ permits a specified ordered pair to have probability as large as $2/25$, while each individual output still has probability $1/5$. Summing over the five equal ordered pairs bounds the collision probability by $2/5$, rather than the $1/5$ required of a universal hash family. Thus an almost strongly universal hash family need not be a universal hash family. Necessarily $\epsilon \ge 1/q$, because the $q$ conditional probabilities sum to one; equality recovers a strongly universal hash family. This relaxation is useful in Wegman–Carter authentication, where efficiency can be traded against a quantified forgery bound within a complete message authentication protocol.

Combine a secret randomly selected almost strongly universal hash family member with a fresh secret output mask. An observed message authentication code tag leaves the hash-selection key hidden, and the conditional hash bound controls substitution forgery probability. The message itself need not be encrypted. Mask reuse can reveal hash differences; repeated-use guarantees require fresh masks and an appropriate analysis of verification information. A one-use polynomial construction needs only two secret field elements, even for a much longer message.