Privacy Model Recommendation System Based on Data Feature Analysis (데이터 특성 분석 기반 프라이버시 모델 추천 시스템)

Seung Hwan Ryu (류승환); Yongki Hong (홍용기); Gihyuk Ko (고기혁); Yang Hee Dong (양희동); Jong Wan Kim (김종완)

doi:10.9708/jksci.2023.28.09.081

Privacy Model Recommendation System Based on Data Feature Analysis

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(9), pp.81-92
DOI : 10.9708/jksci.2023.28.09.081
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 20, 2023
Accepted : September 4, 2023
Published : September 27, 2023

Seung Hwan Ryu ¹, Yongki Hong ¹, Gihyuk Ko ², Yang Hee Dong ¹, Jong Wan Kim ³

¹한국과학기술원
²한국과학기술원 정보전자연구소
³삼육대학교

Accredited

ABSTRACT

A privacy model is a technique that quantitatively restricts the possibility and degree of privacy breaches through privacy attacks. Representative models include k-anonymity, l-diversity, t-closeness, and differential privacy. While many privacy models have been studied, research on selecting the most suitable model for a given dataset has been relatively limited. In this study, we develop a system for recommending the suitable privacy model to prevent privacy breaches. To achieve this, we analyze the data features that need to be considered when selecting a model, such as data type, distribution, frequency, and range. Based on privacy model background knowledge that includes information about the relationships between data features and models, we recommend the most appropriate model. Finally, we validate the feasibility and usefulness by implementing a recommendation prototype system.

KEYWORDS

Privacy model, Data Privacy, De-identification, Background knowledge, Data feature analysis, Recommendation

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Privacy Model Recommendation System Based on Data Feature Analysis

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Privacy Model Recommendation System Based on Data Feature Analysis

ABSTRACT

KEYWORDS

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