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Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining

M Sumana, K S Hareesh
2010 International Journal of Computer Applications  
Privacy Preserving Data mining techniques depends on privacy, which captures what information is sensitive in the original data and should therefore be protected from either direct or indirect disclosure  ...  Secrecy and anonymity are useful ways of thinking about privacy. This privacy should be measureable and entity to be considered private should be valuable.  ...  INTRODUCTION Privacy Preserving Data Mining performs data mining on the private data.  ... 
doi:10.5120/1113-1457 fatcat:utndadf4mzdq5ltzk2r624cx6u

Privacy-Preserving in Data Mining using Anonymity Algorithm for Relational Data

2016 International Journal of Science and Research (IJSR)  
A privacy protection mechanism can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity, against identity and attribute  ...  Data mining is the process of analyzing data from different perspectives. To summarize it into useful information, we can consider several algorithms.  ...  Data mining techniques are used in business and research and are becoming more and more popular with time. Confidentiality issues in data mining.  ... 
doi:10.21275/v5i3.nov162221 fatcat:ihw6w2chtvg2totz36shzqjzm4

Anonymizing Social Networks [chapter]

2010 Chapman & Hall/CRC Data Mining and Knowledge Discovery Series  
In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data.  ...  Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data.  ...  The data trustee hides G, publishing in its place an anonymized graph. We begin by studying naive anonymization, in which the nodes of G are renamed and the structure of the graph is unmodified.  ... 
doi:10.1201/9781420091502-c15 fatcat:52cze3ar5rfh7iyzgy7gkmu6oy

Publishing anonymous survey rating data

Xiaoxun Sun, Hua Wang, Jiuyong Li, Jian Pei
2010 Data mining and knowledge discovery  
The methods are applied to two real-life data sets to demonstrate their efficiency and practical utility.  ...  The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy.  ...  mining environment for anonymous data sets.  ... 
doi:10.1007/s10618-010-0208-4 fatcat:uymtleeayjcxzh3vdcanbnyhlu

Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

Abou-el-ela Abdou Hussien, Nermin Hamza, Hesham A. Hefny
2013 Journal of Information Security  
The key issues were how to modify the data and how to recover the data mining result from the modified data.  ...  In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing.  ...  In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining.  ... 
doi:10.4236/jis.2013.42012 fatcat:drdluik2c5bxbi67og5lkcklfe

Efficient anonymity-preserving data collection

Justin Brickell, Vitaly Shmatikov
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
The output of a data mining algorithm is only as good as its inputs, and individuals are often unwilling to provide accurate data about sensitive topics such as medical history and personal finance.  ...  This makes it especially suitable for data mining scenarios with a large number of respondents.  ...  In order to be practical for use in data mining applications with large numbers of respondents, protocols for anonymous data collection need to be efficient as well as secure.  ... 
doi:10.1145/1150402.1150415 dblp:conf/kdd/BrickellS06 fatcat:4ialv7oauvbwvbbd5a6nfxm35e

Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation

Josep Domingo-Ferrer, Vicenç Torra
2005 Data mining and knowledge discovery  
k-Anonymity is a useful concept to solve the tension between data utility and respondent privacy in individual data (microdata) protection.  ...  Since attributes leading to disclosure (and thus needing kanonymization) may be nominal, ordinal and also continuous, it is important to devise k-anonymization procedures which preserve the semantics of  ...  Acknowledgments Francesc Sebé's help in obtaining the results reported for continuous data is gratefully acknowledged. Comments by William Winkler were also particularly useful to improve this paper.  ... 
doi:10.1007/s10618-005-0007-5 fatcat:q3rq3iqkhra7thnxk7iiep5424

Anonymizing healthcare data

Noman Mohammed, Benjamin C.M. Fung, Patrick C.K. Hung, Cheuk-kwong Lee
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
Experiments on the real-life data demonstrate that our anonymization algorithm can effectively retain the essential information in anonymous data for data analysis and is scalable for anonymizing large  ...  challenges that make traditional data anonymization methods not applicable.  ...  The research is supported in part by Discovery Grants (356065-2008) and Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council of Canada.  ... 
doi:10.1145/1557019.1557157 dblp:conf/kdd/MohammedFHL09 fatcat:quysjrxierbwlfucq2r4a3s4oy

Anonymity-preserving data collection

Zhiqiang Yang, Sheng Zhong, Rebecca N. Wright
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties.  ...  Protection of privacy has become an important problem in data mining.  ...  Data Mining Tools Anonymity-Preserving Data Collection Private and Authenticated Communication Channel Database and Operating System Figure 1: System Components In this paper, we take into account that  ... 
doi:10.1145/1081870.1081909 dblp:conf/kdd/YangZW05 fatcat:ft5g7tjcufct3kub3bxhlazshi

Anonymizing sequential releases

Ke Wang, Benjamin C. M. Fung
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification.  ...  An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately.  ...  Entity matching has been studied in database, data mining, AI and Web communities for information integration, natural language processing and Semantic Web.  ... 
doi:10.1145/1150402.1150449 dblp:conf/kdd/WangF06 fatcat:o7vz3iakdbfbxcbmj6bksoidh4

Workload-aware anonymization

Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan
2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06  
This paper provides a suite of anonymization algorithms that produce an anonymous view based on a target class of workloads, consisting of one or more data mining tasks, as well as selection predicates  ...  Anonymization algorithms typically aim to protect individual privacy, with minimal impact on the quality of the resulting data.  ...  Acknowledgments Our thanks to Bee-Chung Chen, Hector Corrada Bravo, Ted Wild, and Jude Shavlik for insightful conversations, to Jesse Davis for comments on an earlier draft of this paper, and to Benjamin  ... 
doi:10.1145/1150402.1150435 dblp:conf/kdd/LeFevreDR06 fatcat:kf36lh76tjfmtoxsqud5ozs5rm

Ontology-Enhanced Interactive Anonymization in Domain-Driven Data Mining Outsourcing

Brian C.S. Loh, Patrick H.H. Then
2010 2010 Second International Symposium on Data, Privacy, and E-Commerce  
The research objective is to create an ontology-based constrained anonymization framework which aims to preserve meaningful and actionable models for domain-driven data mining while protecting privacy.  ...  This thesis focuses on the data mining outsourcing scenario whereby a data owner publishes data to an application service provider who returns mining results.  ...  and domain driven data mining perspective.  ... 
doi:10.1109/isdpe.2010.7 fatcat:euaht3j43rcoxkmaqda6bjsqi4

The effect of homogeneity on the computational complexity of combinatorial data anonymization

Robert Bredereck, André Nichterlein, Rolf Niedermeier, Geevarghese Philip
2012 Data mining and knowledge discovery  
Complementing previous work, we introduce two new "data-driven" parameterizations for k-Anonymity-the number t in of different input rows and the number t out of different output rowsboth modeling aspects  ...  of data homogeneity.  ...  Samarati and Sweeney (1998) , Samarati (2001) , and Sweeney (2002b) devised the notion of k-anonymity to better quantify the degree of anonymity in sanitized data.  ... 
doi:10.1007/s10618-012-0293-7 fatcat:axmlprhh7be4vcconbhu7jhgle

On the privacy of anonymized networks

Pedram Pedarsani, Matthias Grossglauser
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
Recent work demonstrates that anonymizing node identities may not be sufficient to keep the network private: the availability of node and link data from another domain, which is correlated with the anonymized  ...  The proliferation of online social networks, and the concomitant accumulation of user data, give rise to hotly debated issues of privacy, security, and control.  ...  Acknowledgments We are indebted to Mohamed Kafsi and Patrick Thiran for fruitful discussions and feedback on a draft of the manuscript.  ... 
doi:10.1145/2020408.2020596 dblp:conf/kdd/PedarsaniG11 fatcat:xyoztqppczejxe3rbb2ypfbt2u

Anonymizing set-valued data by nonreciprocal recoding

Mingqiang Xue, Panagiotis Karras, Chedy Raïssi, Jaideep Vaidya, Kian-Lee Tan
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
Anonymizing such data implies ensuring that an adversary should not be able to (1) identify an individual's record, and (2) infer a sensitive label, if such exists.  ...  and recasts data values in a nonreciprocal manner; formally, the bipartite graph from original to anonymized records does not have to be composed of disjoint complete subgraphs.  ...  Acknowledgments We thank Gabriel Ghinit , a who shared the CAHD code with us, and the anonymous reviewers for their constructive feedback.  ... 
doi:10.1145/2339530.2339696 dblp:conf/kdd/XueKRVT12 fatcat:hchwkwatzzcohkq2an2idnykmm
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