In recent years, privacy breaches have been a great concern on the published data. Only removing one’s personal identification information is not sufficient to protect individual’s privacy. Privacy preservation technology for published data is devoted to preventing re-identification and retaining the useful information in published data. In this work, we propose a novel algorithm to deal with sensitive and quasi-identifier items, respectively, in transactional data. The proposed algorithm maintains at least the same or a stronger privacy level for transactional data with 1/k. In numerical experiments, our proposed algorithm shows better running time and better data utility.

中文翻译：

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交易中灵活的敏感K匿名化

近年来，隐私泄露已成为已发布数据的重大关注点。仅删除个人身份信息不足以保护个人隐私。用于已发布数据的隐私保护技术致力于防止重新识别并在已发布数据中保留有用信息。在这项工作中，我们提出了一种新颖的算法来分别处理交易数据中的敏感项目和准标识符项目。对于1 / k的交易数据，所提出的算法至少保持相同或更高的隐私级别。在数值实验中，我们提出的算法显示了更好的运行时间和更好的数据实用性。