{"title":"Privacy Preserving Identity Federation: A Literature Study","authors":"Anne Bumiller, Elisavet Kozyri, Håvard Dagenborg","doi":"10.1145/3745018","DOIUrl":null,"url":null,"abstract":"Within an <jats:italic toggle=\"yes\">Identity federation (IF)</jats:italic> system, users gain access to multiple <jats:italic toggle=\"yes\">Service Providers</jats:italic> (SPs) by submitting credentials issued by one or more <jats:italic toggle=\"yes\">Identity Providers</jats:italic> (IdPs). Such Identity Federations (IFs) raise several privacy concerns: IdPs might track user activity, by recording the accessed services, and SPs might mismanage sensitive user attributes that comprise the submitted credentials. An extensive line of research on <jats:italic toggle=\"yes\">Privacy Preserving</jats:italic> IF has been developed to expose and address these privacy concerns. This survey aims to systematize the privacy requirements and enhancement techniques that has been employed so far in this line of research. Specifically, we use Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) methodologies to organize research work from the last ten years and understand (i) the requirements that privacy-preserving IF is expected to satisfy, (ii) the degree at which these requirements have been formalized, (iii) the techniques employed to enforce these requirements, (iv) the means for providing enforcement assurance, and (v) the degree at which these techniques preserve fundamental authentication objectives and are aligned with existing IF standards. Based on this characterization of the literature, we draw conclusions about the rigorousness of the proposed approaches, their deployability into practice, and lessons learned for future research and practice in the field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"19 1","pages":""},"PeriodicalIF":28.0000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3745018","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Within an Identity federation (IF) system, users gain access to multiple Service Providers (SPs) by submitting credentials issued by one or more Identity Providers (IdPs). Such Identity Federations (IFs) raise several privacy concerns: IdPs might track user activity, by recording the accessed services, and SPs might mismanage sensitive user attributes that comprise the submitted credentials. An extensive line of research on Privacy Preserving IF has been developed to expose and address these privacy concerns. This survey aims to systematize the privacy requirements and enhancement techniques that has been employed so far in this line of research. Specifically, we use Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) methodologies to organize research work from the last ten years and understand (i) the requirements that privacy-preserving IF is expected to satisfy, (ii) the degree at which these requirements have been formalized, (iii) the techniques employed to enforce these requirements, (iv) the means for providing enforcement assurance, and (v) the degree at which these techniques preserve fundamental authentication objectives and are aligned with existing IF standards. Based on this characterization of the literature, we draw conclusions about the rigorousness of the proposed approaches, their deployability into practice, and lessons learned for future research and practice in the field.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.