K-Anonymity using Hierarchical Structure in Indoor Space
نویسندگان
چکیده
منابع مشابه
Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures
Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we stud...
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Anonymity is important in a peer-to-peer system to protect peers that offer/request services. We propose an anonymity scheme on Chord to provide a peer k-anonymity protection against a global passive adversary who can sniff all the communication on a network. For collaborating adversaries, anonymity is protected as long as they perform only passive attacks. An encryption scheme ensures that pee...
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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 available information to re-identify respondent...
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In this note, we describe a method of simulated annealing for producing k-anonymity. For analytic purposes, there is no reason to expect that the method will be superior or worse than the method of applying genetic algorithms used by Iyengar (2002). The main appeal of simulated annealing is the amount of control it allows of the microaggregation process. The k-anonymity problem is known to be N...
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ژورنال
عنوان ژورنال: Journal of Korea Spatial Information Society
سال: 2012
ISSN: 2287-9242
DOI: 10.12672/ksis.2012.20.4.093