In surveys collecting individual data (microdata), each respondent is usually required to report values for a set of attributes. If some of these attributes contain sensitive information, the respondent must trust the collector not to make any inappropriate use of the data and, in case any data are to be publicly released, to properly anonymize them to avoid disclosing sensitive information. If the respondent does not trust the data collector, she may report inaccurately or report nothing at all. The reduce the need for trust, local anonymization is an alternative whereby each respondent anonymizes her data prior to sending them to the data collector. However, local anonymization by each respondent without seeing other respondents' data makes it hard to find a good trade-off minimizing information loss and disclosure risk. We propose a distributed anonymization approach where users collaborate to attain an appropriate level of disclosure protection (and, thus, of information loss). Under our scheme, the final anonymized data are only as accurate as the information released by each respondent; hence, no trust needs to be assumed towards the data collector or any other respondent. Further, if respondents are interested in forming an accurate data set, the proposed collaborative anonymization protocols are self-enforcing and co-utile.
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