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K-Anonymity for Crowdsourcing Database
IEEE Transactions on Knowledge and Data Engineering, 2014In 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
null Sai Wu +4 more
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Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007When 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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2007
To protect respondents’ identity when releasing microdata, data holders often remove or encrypt explicit identifiers, such as names and social security numbers. De-identifying data, however, provide no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly ...
V. Ciriani +3 more
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To protect respondents’ identity when releasing microdata, data holders often remove or encrypt explicit identifiers, such as names and social security numbers. De-identifying data, however, provide no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly ...
V. Ciriani +3 more
openaire +2 more sources
Nibble: An effective k-anonymization
2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 2011K-Anonymity technique is a useful way to protect privacy in information sharing. This paper presents a practical framework for implementing one type of k-anonymization, based on which a greedy algorithm named Nibble for producing approximately minimal generalizations is introduced.
Lei He, Songnian Yu
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2016
Data published online nowadays needs a high level of privacy to gain confidentiality as well as to maintain the privacy laws. The focus on k-anonymity enhancements along the last decade, allows this method to be elected as the starting point of any research.
Rima Kilany +3 more
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Data published online nowadays needs a high level of privacy to gain confidentiality as well as to maintain the privacy laws. The focus on k-anonymity enhancements along the last decade, allows this method to be elected as the starting point of any research.
Rima Kilany +3 more
openaire +1 more source
k-Anonymous Data Mining: A Survey
2008Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming
V. Ciriani +3 more
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Achieving k-Anonymity Privacy Protection Using Generalization and Suppression
Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2002L. Sweeney
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From K-anonymity to Differential Privacy back to K -anonymity!
2013The research community has left no stone unturned in devising strategies for both syntactic and semantic privacy definitions. The literature on privacy protection reveals that no privacy model is capable of incorporating growing demands of data publication (e.g., the adversarial background, needs of data publisher, constraints on underlying dataset etc.
Anjum, Adeel +2 more
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