Streaming algorithms for the missing item finding problem

Many problems on data streams have been studied at two extremes of difficulty: either allowing randomized algorithms, in the static setting (where they should err with bounded probability on the worst case stream); or when only deterministic and infallible algorithms are required. Some recent works have considered the adversarial setting, in which a randomized streaming algorithm must succeed even on data streams provided by an adaptive adversary that can see the intermediate outputs of the algorithm. In order to better understand the differences between these models, we study a streaming task called "Missing Item Finding". In this problem, for $r < n$, one is given a data stream $a_1,\ldots,a_r$ of elements in $[n]$, (possibly with repetitions), and must output some $x \in [n]$ which does not equal any of the $a_i$. We prove that, for $r = n^{Θ(1)}$ and $δ= 1/\mathrm{poly}(n)$, the space required for randomized algorithms that solve this problem in the static setting with error $δ$ is $Θ(\mathrm{polylog}(n))$; for algorithms in the adversarial setting with error $δ$, $Θ((1 + r^2 / n) \mathrm{polylog}(n))$; and for deterministic algorithms, $Θ(r / \mathrm{polylog}(n))$. Because our adversarially robust algorithm relies on free access to a string of $O(r \log n)$ random bits, we investigate a "random start" model of streaming algorithms where all random bits used are included in the space cost. Here we find a conditional lower bound on the space usage, which depends on the space that would be needed for a pseudo-deterministic algorithm to solve the problem. We also prove an $Ω(r / \mathrm{polylog}(n))$ lower bound for the space needed by a streaming algorithm with $< 1/2^{\mathrm{polylog}(n)}$ error against "white-box" adversaries that can see the internal state of the algorithm, but not predict its future random decisions.