International Association for Cryptologic Research

International Association
for Cryptologic Research

IACR News item: 12 April 2024

Alexander May, Massimo Ostuzzi
ePrint Report ePrint Report
Let $\star: G \times X \rightarrow X$ be the action of a group $G$ of size $N=|G|$ on a set $X$. Let $y = g \star x \in X$ be a group action dlog instance, where our goal is to compute the unknown group element $g \in G$ from the known set elements $x,y \in X$. The Galbraith-Hess-Smart (GHS) collision finding algorithm solves the group action dlog in $N^{\frac 1 2}$ steps with polynomial memory. We show that group action dlogs are suitable for precomputation attacks. More precisely, for any $s,t$ our precomputation algorithm computes within $st$ steps a hint of space complexity $s$, which allows to solve any group action dlog in an online phase within $t$ steps. A typical instantiation is $s=t=N^{\frac 1 3}$, which gives precomputation time $N^{\frac 2 3}$ and space $N^{\frac 1 3}$, and online time only $N^{\frac 1 3}$.

Moreover, we show that solving multiple group action dlog instances $y_1, \ldots , y_m$ allows for speedups. Namely, our collision finding algorithm solves $m$ group action dlogs in $\sqrt{m}N^{\frac 1 2}$ steps, instead of the straight-forward $mN^{\frac 1 2}$ steps required for running $m$ times GHS. Interestingly, our multi instance algorithm (with precomputation) can be seen as a special case of our precomputation algorithm. Our multiple instance approach can be freely combined with our precomputations, allowing for a variety of tradeoffs.

Technically, our precomputation and multiple instance group action dlog attacks are adaptations of the techniques from the standard dlog setting in abelian groups. While such an adaptation seems natural, it is per se unclear which techniques transfer from the dlog to the more restricted group dlog setting, for which $X$ does not offer a group structure.

Our algorithms have direct implications for all group action based cryptosystems, such as CSIDH and its variants. We provide experimental evidence that our techniques work well in the CSIDH setting.
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