中文摘要：

在发布同时包含关系和事务属性的数据（简称为关系．事务数据）时，由于关系数据和事务数据均有可能受到链接攻击，需要同时匿名这两部分的数据．现有的数据匿名技术在匿名化关系．事务数据时会造成严重的数据缺损，无法保障数据可用性．针对此问题，提出了（k,l）-多样化模型，通过等价类上的l-多样化约束和事务数据上的肛匿名约束来保证用户隐私不被泄露．在此基础上，设计并实现了APA和PAA两种满足该模型的匿名算法，以不同的顺序对关系-事务数据进行匿名，并提出了相应的数据缺损评估方法．实际公开数据集上的实验结果表明，与现有的数据匿名技术相比，APA和PAA能够在保护用户隐私的前提下，以更低的数据缺损和更高的效率完成对关系-事务数据的匿名．

英文摘要：

When publishing datasets that contain relational and transaction attributes, referred to as RT-data for briefness, either type of data may suffer from linking attacks. Anonymizing both of them is essential However, previous approaches suffer from huge information loss during anonymizing RT-data, and they fail to preserve the utility of datasets. To address this problem, an anonymization model, （k,l）- diversity is proposed to ensure privacy by guaranteeing l-diversity on each equivalence class and k-anonymity on transaction data. In addition, two heuristic algorithms named APA and PAA, which anonymize RT-data in different orders, are also provided to achieve （k,l）- diversity. Extensive experiments based on real-world dataset show that APA and PAA outperform existing approaches in terms of execution time and information loss.