et al., 2015), such an attack does not work a hundred
percent, yet it is obvious that much of the structural
behavior is revealed, including relatively fine details
like the density of points in specific areas of the graph.
In order to prevent such an attack, we can restrict the
number of nodes. As CT is only used for routing and
not display purposes, we can reduce the attack vec-
tor by limiting the number of encrypted nodes, while
parsing an OSM map. This can be done by throw-
ing away unnecessary intermediate nodes, for exam-
ple nodes with a degree ≤ 2.
6 CONCLUSION
We presented cipherPath: Efficient Traversals over
Homomorphically Encrypted Paths, a framework for
the computation of the shortest path in an encrypted
graph. We showed how to construct our framework
based on cryptographic preliminaries and how find
the shortest paths between encrypted nodes. Finally
we analyzed the security and the leakage of our con-
struction. A future direction might be the defense
against more sophisticated graph similarity attacks,
like the neural network approach given by (Bai et al.,
2018). Our goal is to find an upper barrier on the num-
ber of nodes, from which on neural network attacks
become feasible. Another direction of research might
be the sharding of a CT into multiple subsets spread
accross multiple provider.
ACKNOWLEDGEMENTS
This work has been funded by the Fraunhofer Cluster
of Excellence ’Cognitive Internet Technologies’
3
.
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