Injecting purpose and trust into data anonymisation

Most existing works of data anonymisation target at the optimization of the anonymisation metrics to balance the data utility and privacy, whereas they ignore the effects of a requester's trust level and application purposes during the data anonymisation. Our aim of this paper is to propose a much finer level anonymisation scheme with regard to the data requester's trust value and specific application purpose. We prioritize the attributes for anonymisation based on how important and critical they are related to the specified application purposes and propose a trust evaluation strategy to quantify the data requester's reliability, and further build the projection between the trust value and the degree of data anonymiztion, which intends to determine to what extent the data should be anonymized. The decomposition algorithm is developed to find the desired anonymous solution, which guarantees the uniqueness and correctness.

[1]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  L. Cox.  Suppression Methodology and Statistical Disclosure Control , 1980 .

[3]  Samir Khuller,et al.  Achieving anonymity via clustering , 2006, PODS '06.

[4]  Daniel Kifer,et al.  Injecting utility into anonymized datasets , 2006, SIGMOD Conference.

[5]  ASHWIN MACHANAVAJJHALA,et al.  L-diversity: privacy beyond k-anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[6]  Chris Clifton,et al.  Hiding the presence of individuals from shared databases , 2007, SIGMOD '07.

[7]  Raymond Chi-Wing Wong,et al.  (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing , 2006, KDD '06.

[8]  Li Xiong,et al.  Towards Application-Oriented Data Anonymization , 2008 .

[9]  Elisa Bertino,et al.  Privacy Protection , 2022 .

[10]  David J. DeWitt,et al.  Mondrian Multidimensional K-Anonymity , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[11]  Sushil Jajodia,et al.  Toward a multilevel secure relational data model , 1991, SIGMOD '91.

[12]  Jian Pei,et al.  Utility-based anonymization using local recoding , 2006, KDD '06.

[13]  Adam Meyerson,et al.  On the complexity of optimal K-anonymity , 2004, PODS.

[14]  Ninghui Li,et al.  Purpose based access control for privacy protection in relational database systems , 2008, The VLDB Journal.

[15]  Yufei Tao,et al.  Anatomy: simple and effective privacy preservation , 2006, VLDB.

[16]  Roberto J. Bayardo,et al.  Data privacy through optimal k-anonymization , 2005, 21st International Conference on Data Engineering (ICDE'05).

[17]  Philip S. Yu,et al.  Bottom-up generalization: a data mining solution to privacy protection , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[18]  Traian Marius Truta,et al.  Protection : p-Sensitive k-Anonymity Property , 2006 .

[19]  Elisa Bertino,et al.  Micro-views, or on how to protect privacy while enhancing data usability: concepts and challenges , 2006, SGMD.

[20]  Sushil Jajodia,et al.  Towards a Multilevel Secure Relational Data Model , 1991, SIGMOD Conference.

[21]  William E. Winkler,et al.  Using Simulated Annealing for k-anonymity , 2002 .

[22]  Ninghui Li,et al.  t-Closeness: Privacy Beyond k-Anonymity and l-Diversity , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[23]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[24]  Vijay S. Iyengar,et al.  Transforming data to satisfy privacy constraints , 2002, KDD.

[25]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[26]  Philip S. Yu,et al.  Top-down specialization for information and privacy preservation , 2005, 21st International Conference on Data Engineering (ICDE'05).

[27]  Alexandre V. Evfimievski,et al.  Information sharing across private databases , 2003, SIGMOD '03.

[28]  Rajeev Motwani,et al.  Anonymizing Tables , 2005, ICDT.

[29]  Yufei Tao,et al.  Personalized privacy preservation , 2006, Privacy-Preserving Data Mining.

[30]  Chun Zhang,et al.  Storing and querying ordered XML using a relational database system , 2002, SIGMOD '02.

[31]  Qing Zhang,et al.  Aggregate Query Answering on Anonymized Tables , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[32]  Benjamin C. M. Fung,et al.  Anonymizing sequential releases , 2006, KDD '06.

[33]  Raymond Chi-Wing Wong,et al.  Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies , 2008, IEEE Transactions on Knowledge and Data Engineering.

[34]  Yufei Tao,et al.  M-invariance: towards privacy preserving re-publication of dynamic datasets , 2007, SIGMOD '07.

[35]  David J. DeWitt,et al.  Workload-aware anonymization , 2006, KDD '06.

[36]  David J. DeWitt,et al.  Incognito: efficient full-domain K-anonymity , 2005, SIGMOD '05.

[37]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[38]  Ramakrishnan Srikant,et al.  Hippocratic Databases , 2002, VLDB.

[39]  Latanya Sweeney,et al.  Achieving k-Anonymity Privacy Protection Using Generalization and Suppression , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..