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   Articles with "sensitive attributes" as a keyword



An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

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recommendations! 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… read more here.

Keywords: sensitive attributes; multiple sensitive; algorithm individuation; individuation anonymity ... See more keywords

An enhanced dynamic KC-slice model for privacy preserving data publishing with multiple sensitive attributes by inducing sensitivity

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recommendations! 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… read more here.

Keywords: sensitive attributes; multiple sensitive; privacy; slice model ... See more keywords

Identifying Reliability-Critical Primary Inputs of Combinational Circuits Based on the Model of Gate-Sensitive Attributes

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recommendations! 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… read more here.

Keywords: gate sensitive; sensitive attributes; primary; reliability ... See more keywords

Fair Link Prediction With Overlapping Groups

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recommendations! Published in 2025 at "IEEE Transactions on Computational Social Systems"

DOI: 10.1109/tcss.2024.3479702

Abstract: In this article, we introduce FairLPG, a framework for ensuring fairness for the task of link prediction in graphs with multiple sensitive attributes. In the context of link prediction in graphs, the fairness notions of… read more here.

Keywords: prediction; fair link; sensitive attributes; link prediction ... See more keywords

Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness

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recommendations! 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… read more here.

Keywords: quasi identifiers; diversity closeness; anonymization; sensitive attributes ... See more keywords

Protecting Sensitive Attributes by Adversarial Training Through Class-Overlapping Techniques

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recommendations! 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… read more here.

Keywords: attributes adversarial; sensitive attributes; class overlapping; class ... See more keywords

Model-Agnostic Causal Embedding Learning for Counterfactually Group-Fair Recommendation

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recommendations! Published in 2024 at "IEEE Transactions on Knowledge and Data Engineering"

DOI: 10.1109/tkde.2024.3424906

Abstract: Group-fair recommendation aims at ensuring the equality of recommendation results across user groups categorized by sensitive attributes (e.g., gender, occupation, etc.). Existing group-fair recommendation models traditionally employ original user embeddings for both training and testing,… read more here.

Keywords: group fairness; group; fair recommendation; sensitive attributes ... See more keywords

MultiFair: Model Fairness With Multiple Sensitive Attributes

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recommendations! Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"

DOI: 10.1109/tnnls.2024.3384181

Abstract: While existing fairness interventions show promise in mitigating biased predictions, most studies concentrate on single-attribute protections. Although a few methods consider multiple attributes, they either require additional constraints or prediction heads, incurring high computational overhead… read more here.

Keywords: information; fairness multiple; multifair model; sensitive attributes ... See more keywords

Fairness Evaluation of Neural Networks Through Computational Profile Likelihood

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recommendations! Published in 2025 at "Computational Intelligence"

DOI: 10.1111/coin.70124

Abstract: Despite high predictive performance, machine learning models can be unfair towards specific demographic subgroups characterized by sensitive attributes such as gender or race. This paper presents a novel approach using Computational Profile Likelihood (CPL) to… read more here.

Keywords: sensitive attribute; computational profile; profile likelihood; sensitive attributes ... See more keywords

Privacy Preserving Data Publishing for Heterogeneous Multiple Sensitive Attributes with Personalized Privacy and Enhanced Utility

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recommendations! 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);… read more here.

Keywords: sensitive attributes; privacy preserving; data publishing; preserving data ... See more keywords