  For a better understanding of anonymization methods for location traces, we
have designed and held a location trace anonymization contest that deals with a
long trace (400 events per user) and fine-grained locations (1024 regions). In
our contest, each team anonymizes her original traces, and then the other teams
perform privacy attacks against the anonymized traces. In other words, both
defense and attack compete together, which is close to what happens in real
life. Prior to our contest, we show that re-identification alone is
insufficient as a privacy risk and that trace inference should be added as an
additional risk. Specifically, we show an example of anonymization that is
perfectly secure against re-identification and is not secure against trace
inference. Based on this, our contest evaluates both the re-identification risk
and trace inference risk and analyzes their relationship. Through our contest,
we show several findings in a situation where both defense and attack compete
together. In particular, we show that an anonymization method secure against
trace inference is also secure against re-identification under the presence of
appropriate pseudonymization. We also report defense and attack algorithms that
won first place, and analyze the utility of anonymized traces submitted by
teams in various applications such as POI recommendation and geo-data analysis.
