A Hybrid Optimization Approach for Anonymizing Transactional Data

Transactional data about individuals is increasingly being collected to support many important real-life applications ranging from healthcare to marketing. Thus, privacy issues in sharing transactional data among different parties have attracted considerable research interest in recent years. Due to the high-dimensionality and sparsity of transactional data, existing privacy-preserving techniques will incur excessive information loss. We propose a hybrid optimization approach for anonymizing transactional data through integrating different anonymous techniques. Experimental results verify that our approach significantly outperforms the current state-of-the-art algorithms in terms of data utility.

[1]  Wendy Hui Wang,et al.  Towards publishing recommendation data with predictive anonymization , 2010, ASIACCS '10.

[2]  Takao Takenouchi,et al.  Top-down itemset recoding for releasing private complex data , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[3]  Panos Kalnis,et al.  Anonymous Publication of Sensitive Transactional Data , 2011, IEEE Transactions on Knowledge and Data Engineering.

[4]  Panos Kalnis,et al.  On the Anonymization of Sparse High-Dimensional Data , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[5]  Ting Yu,et al.  Anonymizing bipartite graph data using safe groupings , 2008, Proc. VLDB Endow..

[6]  Lie Wang,et al.  A Clustering-Based Bipartite Graph Privacy-Preserving Approach for Sharing High-Dimensional Data , 2014, Int. J. Softw. Eng. Knowl. Eng..

[7]  Aris Gkoulalas-Divanis,et al.  Utility-guided Clustering-based Transaction Data Anonymization , 2012, Trans. Data Priv..

[8]  Spiros Skiadopoulos,et al.  Anonymizing Data with Relational and Transaction Attributes , 2013, ECML/PKDD.

[9]  Bradley Malin,et al.  COAT: COnstraint-based anonymization of transactions , 2010, Knowledge and Information Systems.

[10]  Ron Kohavi,et al.  Real world performance of association rule algorithms , 2001, KDD '01.

[11]  Panos Kalnis,et al.  Privacy-preserving anonymization of set-valued data , 2008, Proc. VLDB Endow..

[12]  Philip S. Yu,et al.  Anonymizing transaction databases for publication , 2008, KDD.

[13]  Nikos Mamoulis,et al.  Non-homogeneous generalization in privacy preserving data publishing , 2010, SIGMOD Conference.

[14]  Ke Wang,et al.  Anonymizing Transaction Data by Integrating Suppression and Generalization , 2010, PAKDD.

[15]  Philip S. Yu,et al.  Privacy-preserving data publishing: A survey of recent developments , 2010, CSUR.

[16]  Mohamed A. Sharaf,et al.  Databases Theory and Applications , 2014, Lecture Notes in Computer Science.

[17]  Li-e Wang,et al.  Personalized Privacy Protection for Transactional Data , 2014, ADMA.

[18]  Panos Kalnis,et al.  Local and global recoding methods for anonymizing set-valued data , 2010, The VLDB Journal.

[19]  Anna Oganian,et al.  A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality , 2006 .

[20]  Tamir Tassa,et al.  k-Anonymization Revisited , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[21]  Jeffrey F. Naughton,et al.  Anonymization of Set-Valued Data via Top-Down, Local Generalization , 2009, Proc. VLDB Endow..