期刊文献+

k-匿名隐私保护模型中不确定性数据的查询问题

Querying of Uncertain Data in the k-anonymity Privacy Protecting Model
下载PDF
导出
摘要 查询是一种重要的数据库操作。在k-匿名隐私保护模型中,每条元组不仅包括精确数据,还包括泛化数据,因此k-匿名数据是一种不确定数据。为了讨论k-匿名数据的查询问题,首先,提出一种描述k-匿名数据的不确定性数据模型,在此基础上,定义了k-匿名数据的成员(Membership)问题、可能性(Possibility)问题、确定性(Certainty)问题、包含(Containment)问题等查询问题,然后,讨论了这些问题的数据复杂度,证明了Membership问题是PTIME,q-Membership问题是NP-完全的,q′-Containment问题是Πp 2-完全的,q-Containment问题coNP-完全的,Possibility问题是PTIME,q-Possibility问题是NP-完全的,Certainty问题是coNP-完全的。这些结论为k-匿名隐私保护模型中不确定性数据查询方法的研究奠定了理论基础。 It is a very important operation to query on databases. Each tuple in the k-anonymity privacy protection model contains not only the accurate data, but also the generalized data, so the k-anonymous data is a kind of uncertain data. In order to discuss the querying problem of k-anonymous data, a k-anonymous data model based on the possible world is proposed first. And then, the querying problems as Membership, Possibility, Certainty, Containment are defined. Finally the data-complexity of these querying problems are investigated, and show that Membership is in PTIME, q-Membership is NP-complete, q^Containment is lip 2-complete, q-Containment is coNP-complete, possibility is in PTIME, q-Possibility is NP-complete, certainty is coNP-complete. These conclusions lay a theoretical foundation for the study of the querying of uncertain data in the k-anonymity privacy protecting model.
出处 《计算机与数字工程》 2013年第11期1779-1783,1865,共6页 Computer & Digital Engineering
基金 国家自然科学基金(编号:61070032)资助
关键词 不确定性数据 K-匿名 可能世界 数据模型 查询 数据复杂度 uncertain data, k-anonymity, possible world, data model, querying, data-complexity
  • 相关文献

参考文献26

  • 1P.Samarati,L.Sweeney.Protecting Privacy When Disclosing Information:K-anonymity and Its Enforcement through Generalization and Suppression[R] .Technical Report SRL-CSL-98-04,SRI Computer Science Laboratory,1998. 被引量:1
  • 2L.Sweeney.K-Anonymity:A Model for Protecting Privacy.International Journal of Uncertainty[J] .Fuzziness and Knowledge-Based Systems,2002,10 (5):557-570. 被引量:1
  • 3Sweeney L.Achieving K-anonymity Privacy Protection Using Generalization and Suppression.International Journal of Uncertainty[J] .Fuzziness and Knowledge-Based Systems,2002,10(5):571-588. 被引量:1
  • 4Fei liu,Yan Jia,Weihong Han.A New K-anonymity Algorithm towards Multiple Sensitive Attributes[C] //CIT 2012:768-722. 被引量:1
  • 5Batya Kenig,Tamir Tassa:A practical approximation algorithm for optimal k-anonymity[J] .Data Min.Knowl.Discov.(DATAMINE),2012,25 (1):134-168. 被引量:1
  • 6Cormode G,Srivastava D.Anonymized data:generation,models,usage[C] //Proceedings of the 35th SIGMOD international Conference on Management of Data.New York,NY,USA:ACM,2009:1015-1018. 被引量:1
  • 7R.Cheng,D.Kalashnikov,S.Prabhakar.Evaluating probabilistic queries over imprecise data[C] //Proc.of SIGMOD,2003. 被引量:1
  • 8Pei J,Jiang B,Lin X,et al.Probabilistic skylines on uncertain data[C] //Proc of Inc Conf on Very large data bases(VLDB).New York:ACM,2007:15-26. 被引量:1
  • 9Atallah M J,Qi Y.Computing all skyline probabilities for uncertain data[C] //Proc of ACM SIGMOD-SIGACT-SIGART Symp on Principles of Database Systems(PODS).New York:ACM,2009:279-287. 被引量:1
  • 10Cheng R,Chen J,Mokbel M,et al.Probabilistic verifiers:evaluating constrained nearest-neighbor queries over uncertain data[C] //Proc of Int Conf on Data Engineering(ICDE).Piscataway,NJ:IEEE,2008:973-982. 被引量:1

二级参考文献12

  • 1Singh S,Mayfield C,Shah R,et al.Query selectivity estimation for uncertain data[C] //Proc of the 20th Int Conf on Scientific and Statistical Database Management (SSDBM'08).New York:Springer,2008:61-78. 被引量:1
  • 2Abiteboul S,Kanellakis P,Grahne G.On the representation and querying of set of possible worlds[J].ACM SIGMOD Record,1987,16(3):34-48. 被引量:1
  • 3Cheng R,Kalashnikov D V,Prabhakar S.Evaluating probabilistic queries over imprecise data[C] //Proc of the 2003 ACM SIGMOD Int Conf on Management of Data (SIGMOD'03).New York:ACM,2003:551-562. 被引量:1
  • 4Soliman M A,Ilyas I F,Chang K C -C.Top-k query processing in uncertain databases[C] //Proc of the 23rd Int Conf on Data Engineering (ICDE'07).Piscataway,NJ:IEEE,2007:896-905. 被引量:1
  • 5Hua M,Pei J,Zhang W,et al.Ranking queries on uncertain data:A probabilistic threshold approach[C] //Proc ACM Int Conf on Management of Data (SIGMOD'08).New York:ACM,2008:673-686. 被引量:1
  • 6Jin Che-Qing,Yi Ke,Chen Lei,et al.Sliding-window top-k queries on uncertain streams[J].Proc of VLDB Endowment,2008,1(1):301-312. 被引量:1
  • 7Cheng R,Chen J,Mokbel M,et al.Probabilistic Verifiers:Evaluating constrained nearest-neighbor queries over uncertain data[C] //Proc of Int Conf on Data Engineering (ICDE 2008).Piscataway,NJ:IEEE,2008:973-982. 被引量:1
  • 8Qi Y,Singh S,Shah R,et al.Indexing probabilistic nearest-neighbor threshold queries[C] //Proc of the 1st Workshop on Management of Uncertain Data (MUD'08).Enschede,Netherlands:Centre for Telematics and Information Technology,2008:87-102. 被引量:1
  • 9George Beskales,Mohamed A.Soliman,Ihab F.Ilyas.Efficient search for the top-k probable nearest neighbors in uncertain databases[J].Proc of VLDB Endowment,2008,1(1):326-339. 被引量:1
  • 10Feifei Li,Ke Yi,Jeffrey Jestes.Ranking Distributed Probabilistic Data[C] //Proc of SIGMOD Conference.New York:ACM,2009:361-374. 被引量:1

共引文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部