Privacy-Preserving Trajectory Data Publishing from Adversary with Limited Information

情報処理学会

説明

Privacy issues obstacles data publication because sanitizing data to satisfy privacy notions eventually lowers the data usefulness. For trajectory data, it is challenging to remove privacy threat and to retain data usefulness at the same time because of its high dimensionality and sequentiality. Additionally, anonymizing trajectory data has two conflicting goals of k-anonymity and l-diversity. Many researches had limited focus on k-anonymity for trajectory data, in this paper, we consider both k-anonymity and l-diversity by supposing a weak adversary who has limited information of pre-determined locations. We sanitize data not to allow the adversary to know victim's private doublets.

収録刊行物

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