Flash:高效、稳定、最优的k -匿名

F. Kohlmayer, F. Prasser, C. Eckert, A. Kemper, K. Kuhn
{"title":"Flash:高效、稳定、最优的k -匿名","authors":"F. Kohlmayer, F. Prasser, C. Eckert, A. Kemper, K. Kuhn","doi":"10.1109/SocialCom-PASSAT.2012.52","DOIUrl":null,"url":null,"abstract":"K-anonymization is an important technique for the de-identification of sensitive datasets. In this paper, we briefly describe an implementation framework which has been carefully engineered to meet the needs of an important class of k-anonymity algorithms. We have implemented and evaluated two major well-known algorithms within this framework and show that it allows for highly efficient implementations. Regarding their runtime behaviour, we were able to closely reproduce the results from previous publications but also found some algorithmic limitations. Furthermore, we propose a new algorithm that achieves very good performance by implementing a novel strategy and exploiting different aspects of our implementation framework. In contrast to the current state-of-the-art, our algorithm offers algorithmic stability, with execution time being independent of the actual representation of the input data. Experiments with different real-world datasets show that our solution clearly outperforms the previous algorithms.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"66","resultStr":"{\"title\":\"Flash: Efficient, Stable and Optimal K-Anonymity\",\"authors\":\"F. Kohlmayer, F. Prasser, C. Eckert, A. Kemper, K. Kuhn\",\"doi\":\"10.1109/SocialCom-PASSAT.2012.52\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"K-anonymization is an important technique for the de-identification of sensitive datasets. In this paper, we briefly describe an implementation framework which has been carefully engineered to meet the needs of an important class of k-anonymity algorithms. We have implemented and evaluated two major well-known algorithms within this framework and show that it allows for highly efficient implementations. Regarding their runtime behaviour, we were able to closely reproduce the results from previous publications but also found some algorithmic limitations. Furthermore, we propose a new algorithm that achieves very good performance by implementing a novel strategy and exploiting different aspects of our implementation framework. In contrast to the current state-of-the-art, our algorithm offers algorithmic stability, with execution time being independent of the actual representation of the input data. Experiments with different real-world datasets show that our solution clearly outperforms the previous algorithms.\",\"PeriodicalId\":129526,\"journal\":{\"name\":\"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"66\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SocialCom-PASSAT.2012.52\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SocialCom-PASSAT.2012.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 66

摘要

k -匿名化是敏感数据集去识别化的一项重要技术。在本文中,我们简要描述了一个经过精心设计的实现框架,以满足一类重要的k-匿名算法的需求。我们已经在这个框架中实现和评估了两个主要的知名算法,并表明它允许高效的实现。关于它们的运行时行为,我们能够从以前的出版物中重现结果,但也发现了一些算法限制。此外,我们提出了一种新的算法,通过实现一种新的策略和利用我们的实现框架的不同方面来实现非常好的性能。与当前最先进的算法相比,我们的算法提供了算法稳定性,执行时间独立于输入数据的实际表示。不同真实数据集的实验表明,我们的解决方案明显优于以前的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flash: Efficient, Stable and Optimal K-Anonymity
K-anonymization is an important technique for the de-identification of sensitive datasets. In this paper, we briefly describe an implementation framework which has been carefully engineered to meet the needs of an important class of k-anonymity algorithms. We have implemented and evaluated two major well-known algorithms within this framework and show that it allows for highly efficient implementations. Regarding their runtime behaviour, we were able to closely reproduce the results from previous publications but also found some algorithmic limitations. Furthermore, we propose a new algorithm that achieves very good performance by implementing a novel strategy and exploiting different aspects of our implementation framework. In contrast to the current state-of-the-art, our algorithm offers algorithmic stability, with execution time being independent of the actual representation of the input data. Experiments with different real-world datasets show that our solution clearly outperforms the previous algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信