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The authors study the distributed k-anonymous data collection problem: a data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data record, they say that the data collection process is k-anonymous if it allows the data collector to obtain a k-anonymized version of T without revealing the original records to any adversary. In contrast, to most k-anonymization approaches which trust the data collector, the work assumes that the adversary can be any third party, including the data collector and the other responders.
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