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Published in 2017 at "Wireless Personal Communications"
DOI: 10.1007/s11277-016-3922-4
Abstract: AbstractAt present, most of privacy preserving approaches in data publishing are applied to single sensitive attribute. However, applying single-sensitive-attribute privacy preserving techniques directly into data with multiple sensitive attributes often causes leakage of large amount…
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Keywords:
sensitive attributes;
multiple sensitive;
algorithm individuation;
individuation anonymity ... See more keywords
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Published in 2018 at "Journal of King Saud University - Computer and Information Sciences"
DOI: 10.1016/j.jksuci.2018.09.013
Abstract: Abstract Privacy Preserving Data Publishing (PPDP) is an important aspect of real world scenarios. PPDP moves the researcher in the right direction by maintaining privacy and utility trade-off while publishing the data. This paper presents…
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Keywords:
sensitive attributes;
multiple sensitive;
privacy;
slice model ... See more keywords
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Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3142194
Abstract: The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the…
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Keywords:
gate sensitive;
sensitive attributes;
primary;
reliability ... See more keywords
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Published in 2019 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2017.2698472
Abstract: A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs…
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Keywords:
quasi identifiers;
diversity closeness;
anonymization;
sensitive attributes ... See more keywords
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Published in 2023 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3236180
Abstract: In recent years, machine learning as a service (MLaaS) has brought considerable convenience to our daily lives. However, these services raise the issue of leaking users’ sensitive attributes, such as race, when provided through the…
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Keywords:
attributes adversarial;
sensitive attributes;
class overlapping;
class ... See more keywords
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Published in 2020 at "Systematic Reviews in Pharmacy"
DOI: 10.31838/srp.2020.9.151
Abstract: In recent years, personal data availability has become vast, which leads to the concept of Privacy-preserving. Privacy-Preserving is an essential issue in all research fields. Many privacy methods are available for privacy-preserving data publishing (PPDP);…
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Keywords:
sensitive attributes;
privacy preserving;
data publishing;
preserving data ... See more keywords