Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Versatile publishing for privacy preservation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
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Existing approaches on privacy-preserving data publishing rely on the assumption that data can be divided into quasi-identifier attributes (QI) and sensitive attribute (SA). This assumption does not hold when an attribute has both sensitive values and identifying values, which is typically the case. In this paper, we study how such attributes would impact the privacy model and data anonymization. We identify a new form of attacks, called "freeform attacks", that occur on such data without explicit QI attributes and SA attributes. We present a framework for modeling identifying/sensitive information at the value level, define a problem to eliminate freeform attacks, and outline an efficient solution.