DC FieldValueLanguage
dc.contributor.authorStefanović, Tamaraen_US
dc.contributor.authorGhilezan, Silviaen_US
dc.date.accessioned2021-09-01T11:20:03Z-
dc.date.available2021-09-01T11:20:03Z-
dc.date.issued2021-04-01-
dc.identifier.isbn978-3-030-72465-8-
dc.identifier.issn1868-4238-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4652-
dc.description.abstractCaller identification (or Caller ID) is a telephone service that transmits a caller’s phone number to a receiving party’s telephony equipment when the call is being set up. Besides the telephone number, the Caller ID service may transmit a name associated with the calling telephone number. The appearance of the first Caller ID devices caused suspicion among the users and the public because of the potential security and privacy issues that the caller identification may cause. Privacy issues apply to users of the Caller ID applications, but also to non-users whose phone numbers are stored in the database of some Caller ID application. The emergence of the data privacy laws has led discussions on the privacy policies of Caller ID applications and their compliance with the law. In this paper we investigate two Caller ID applications, Truecaller and Everybody, and compliance of their privacy policies with the data privacy laws, especially the GDPR, the ePrivacy Directive and the ePrivacy Regulation. Further, we deal in more detail with the data privacy problem of non-users and we give the connection between those problems, and the inverse privacy problem. In order to solve the privacy problem of non-users, we develop the mathematical model based on the notions of privacy variables and sensitivity function. Finally, we discussed open questions related to the identity protection of Caller ID app users and non-users, and their trust in Caller ID apps.en_US
dc.publisherSpringer Linken_US
dc.relationAdvanced artificial intelligence techniques for analysis and design of system components based on trustworthy BlockChain technology - AI4TrustBCen_US
dc.relation.ispartofseriesIFIP Advances in Information and Communication Technologyen_US
dc.subjectCaller ID applications | GDPR | Inverse privacy | Name sensitivity | Privacy policy | Privacy variablesen_US
dc.titlePreserving Privacy in Caller ID Applicationsen_US
dc.typeConference Paperen_US
dc.relation.conferenceIFIP International Summer School on Privacy and Identity Managementen_US
dc.relation.publicationPrivacy and Identity Managementen_US
dc.identifier.doi10.1007/978-3-030-72465-8_9-
dc.identifier.scopus2-s2.0-85107360426-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85107360426-
dc.contributor.affiliationMathematicsen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage151-
dc.relation.lastpage168-
dc.description.rankM33-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeConference Paper-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/AI4TrustBC/description.php-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/AI4TrustBC/participants.php-
crisitem.author.orcid0000-0003-2253-8285-
Show simple item record

SCOPUSTM   
Citations

1
checked on Nov 23, 2024

Page view(s)

20
checked on Nov 23, 2024

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.