Results 241 to 250 of about 9,107,365 (284)

Differentially Private K-Anonymity

2014 12th International Conference on Frontiers of Information Technology, 2014
Research in privacy preserving data publication can be broadly categorized in two classes. Syntactic privacy definitions have been under the cursor of the research community for the past many years. A lot of research is primarily dedicated to developing algorithms and notions for syntactic privacy that thwart the re-identification attacks.
Adeel Anjum, Adnan Anjum
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On Distributed k-Anonymization

Fundamenta Informaticae, 2009
When a database owner needs to disclose her data, she can k-anonymize her data to protect the involved individuals' privacy. However, if the data is distributed between two owners, then it is an open question whether the two owners can jointly k-anonymize the union of their data, such that the information suppressed in one owner's data is not revealed ...
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A Hybrid Method for k-Anonymization

2008 IEEE Asia-Pacific Services Computing Conference, 2008
K-anonymity is a model to protect public released microdata from individual identification. It requires that each record is identical to at least k-1 other records in the anonymized dataset with respect to a set of privacy-related attributes. Although it is easy to anonymize the original dataset to satisfy the requirement of k-anonymity, it is ...
Jun-Lin Lin, Meng-Cheng Wei, Chih-Wen Li, Kuo-Chiang Hsieh   +3 more
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On the complexity of optimal K-anonymity

Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2004
The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. We prove that two general versions of optimal k-anonymization of relations are NP-hard, including the suppression version which amounts to choosing a minimum number of entries to ...
Adam Meyerson, Ryan Williams 0001
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Approximate algorithms for K-anonymity

Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007
When a table containing individual data is published, disclosure of sensitive information should be prohibitive. A naive approach for the problem is to remove identifiers such as name and social security number. However, linking attacks which joins the published table with other tables on some attributes, called quasi-identifier, may reveal the ...
Hyoungmin Park, Kyuseok Shim
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K-Anonymity for Crowdsourcing Database

IEEE Transactions on Knowledge and Data Engineering, 2014
In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consist of a set of database records and corresponding questions for human workers. The
Sai Wu, Xiaoli Wang 0002, Sheng Wang 0011, Zhenjie Zhang, Anthony K. H. Tung   +4 more
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The Classification of k-anonymity Data

2011 Seventh International Conference on Computational Intelligence and Security, 2011
In recent years, anonymization methods have emerged as an important tool to preserver individual privacy when relasing privacy sensitive data. All of these methods are under different privacy and utility assumption. But there has been little research addressing how to effectively use the anonymized data for data mining.
Bingchun Lin, Guohua Liu
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Clustering-Based k-Anonymity

2012
Privacy is one of major concerns when data containing sensitive information needs to be released for ad hoc analysis, which has attracted wide research interest on privacy-preserving data publishing in the past few years. One approach of strategy to anonymize data is generalization.
Xianmang He, Huahui Chen 0001, Yefang Chen, Yihong Dong, Peng Wang 0027, Zhenhua Huang 0005   +5 more
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k-anonymous message transmission

Proceedings of the 10th ACM conference on Computer and communications security, 2003
Informally, a communication protocol is sender k - anonymous if it can guarantee that an adversary, trying to determine the sender of a particular message, can only narrow down its search to a set of k suspects. Receiver k-anonymity places a similar guarantee on the receiver: an adversary, at best, can only narrow down the possible receivers to a set ...
Luis von Ahn, Andrew Bortz, Nicholas J. Hopper   +2 more
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A Personalized (a,k)-Anonymity Model

2008 The Ninth International Conference on Web-Age Information Management, 2008
One important privacy principle is that an individual has the freedom to decide his/her own privacy preferences, which should be taken into account when data holders release their privacy preserving micro data. Nevertheless, current related k-anonymity model research focuses on protecting individual private information by using pre-defined constraint ...
Xiaojun Ye, Yawei Zhang, Ming Liu
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