It is important to manage individuals’ personal data after their death to maintain their dignity or follow their wishes as much as possible. From this perspective, this report describes the real-world commercialization of immortal digital personalities, which gives eternal life to the deceased in a digital form. We identify the problems with the commercialization of deceased users’ images and personal data, which becomes postmortem entertainment. Considering these problems, we seek out the ideal form of deceased users’ personal data for commercialization. We conduct a social survey to understand how ordinary Japanese people feel about the various types of publicly available services that use personal data after death, such as social network service logs. By analyzing our survey results approximately 20% of respondents would allow the commercial use of their personal data, such as browsing their social network service logs, if they could receive compensation during their lifetime.

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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.