  In the decremental single-source shortest paths (SSSP) problem, the input is
an undirected graph $G=(V,E)$ with $n$ vertices and $m$ edges undergoing edge
deletions, together with a fixed source vertex $s\in V$. The goal is to
maintain a data structure that supports shortest-path queries: given a vertex
$v\in V$, quickly return an (approximate) shortest path from $s$ to $v$. The
decremental all-pairs shortest paths (APSP) problem is defined similarly, but
now the shortest-path queries are allowed between any pair of vertices of $V$.
Both problems have been studied extensively since the 80's, and algorithms with
near-optimal total update time and query time have been discovered for them.
Unfortunately, all these algorithms are randomized and, more importantly, they
need to assume an oblivious adversary.
  Our first result is a deterministic algorithm for the decremental SSSP
problem on weighted graphs with $O(n^{2+o(1)})$ total update time, that
supports $(1+\epsilon)$-approximate shortest-path queries, with query time
$O(|P|\cdot n^{o(1)})$, where $P$ is the returned path. This is the first
$(1+\epsilon)$-approximation algorithm against an adaptive adversary that
supports shortest-path queries in time below $O(n)$, that breaks the $O(mn)$
total update time bound of the classical algorithm of Even and Shiloah from
1981.
  Our second result is a deterministic algorithm for the decremental APSP
problem on unweighted graphs that achieves total update time
$O(n^{2.5+\delta})$, for any constant $\delta>0$, supports approximate distance
queries in $O(\log\log n)$ time; the algorithm achieves an
$O(1)$-multiplicative and $n^{o(1)}$-additive approximation on the path length.
All previous algorithms for APSP either assume an oblivious adversary or have
an $\Omega(n^{3})$ total update time when $m=\Omega(n^{2})$.
