Privacy Preserving Data Mining on Unstructured Data

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

[1]  Kunal Talwar,et al.  Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

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

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

[4]  Jameela Al-Jaroodi,et al.  Applications of big data to smart cities , 2015, Journal of Internet Services and Applications.

[5]  Vitaly Shmatikov,et al.  Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[6]  Li Xiong,et al.  A two-phase algorithm for mining sequential patterns with differential privacy , 2013, CIKM.

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

[8]  M. B. Malik,et al.  Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects , 2012, 2012 Third International Conference on Computer and Communication Technology.

[9]  Ninghui Li,et al.  PrivBasis: Frequent Itemset Mining with Differential Privacy , 2012, Proc. VLDB Endow..

[10]  Benjamin C. M. Fung,et al.  Differentially private transit data publication: a case study on the montreal transportation system , 2012, KDD.

[11]  Xiang Cheng,et al.  Differentially Private Frequent Itemset Mining via Transaction Splitting , 2015, IEEE Trans. Knowl. Data Eng..

[12]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[13]  Charles Duhigg,et al.  How Companies Learn Your Secrets , 2012 .

[14]  Xiaofeng Meng,et al.  Differentially Private Set-Valued Data Release against Incremental Updates , 2013, DASFAA.

[15]  Hervais Simo.  Big Data: Opportunities and Privacy Challenges , 2015 .

[16]  Ninghui Li,et al.  Differentially private grids for geospatial data , 2012, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

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

[18]  Benjamin C. M. Fung,et al.  Publishing set-valued data via differential privacy , 2011, Proc. VLDB Endow..

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

[20]  Alessandro Acquisti,et al.  Predicting Social Security numbers from public data , 2009, Proceedings of the National Academy of Sciences.

[21]  Ting Yu,et al.  Mining frequent graph patterns with differential privacy , 2013, KDD.

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

[23]  Cyrus Shahabi,et al.  A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing , 2014, Proc. VLDB Endow..

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