![](bgb.png)
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
10
20
ρ
U,b
|U
g
| [#]
Figure 11: The anonymity set size in dependence of the
probability of blocked users.
decentralized. We tackle to balance both require-
ments, privacy and integrity.
Our evaluation was done with Veins showing that
HGM improves the privacy of users by hiding them
along with other members in regions with uniform
probability. Among other things, the results also show
that our approach is robust in terms of dishonest par-
ticipants who suppress messages. We also show that
the overhead related to HGM is acceptable.
In future work we would like to extend our anal-
ysis to a more realistic scenario such as (Codeca
et al., 2016). We want to increase performance using
WAVE’s channel switching in the simulation. In addi-
tion, we want to strengthen our attacker by including
external knowledge such as derived user behavior and
analyze tracers.
ACKNOWLEDGEMENTS
This research was partly funded by the German Fed-
eral Ministry of Education and Research (BMBF)
with grant number: 16KIS0367K.
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