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Derived Partial Identities Generated from App Permissions
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (PriSec)ORCID-id: 0000-0002-0418-4121
Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), Institutionen för matematik och datavetenskap (from 2013). (PriSec)ORCID-id: 0000-0002-5235-5335
2017 (engelsk)Inngår i: Open Identity Summit 2017: Proceedings / [ed] Lothar Fritsch, Heiko Roßnagel, Detlef Hühnlein, Bonn: Gesellschaft für Informatik, 2017, s. 117-130Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This article presents a model of partial identities derived from app permissions that is based on Pfitzmann and Hansen’s terminology for privacy [PH10]. The article first shows how app permissions accommodate the accumulation of identity attributes for partial digital identities by building a model for identity attribute retrieval through permissions. Then, it presents an experimental survey of partial identity access for selected app groups. By applying the identity attribute retrieval model on the permission access log from the experiment, we show how apps’ permission usage is providing to identity profiling.

sted, utgiver, år, opplag, sider
Bonn: Gesellschaft für Informatik, 2017. s. 117-130
Serie
Lecture Notes in Informatics (LNI), ISSN 1617-5468 ; 277
Emneord [en]
identity management, Partial Identity, Access Control, Apps, Permissions, Privacy, Data
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
URN: urn:nbn:se:kau:diva-63724ISBN: 978-3-88579-671-8 (tryckt)OAI: oai:DiVA.org:kau-63724DiVA, id: diva2:1141681
Konferanse
Open Identity Summit (OID) 2017, 5-6 october 2017, Karlstad, Sweden.
Tilgjengelig fra: 2017-09-15 Laget: 2017-09-15 Sist oppdatert: 2019-11-11bibliografisk kontrollert
Inngår i avhandling
1. Towards Measuring Apps' Privacy-Friendliness
Åpne denne publikasjonen i ny fane eller vindu >>Towards Measuring Apps' Privacy-Friendliness
2018 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Today's phone could be described as a charismatic tool that has the ability to keep human beings captivated for a considerable amount of their precious time. Users remain in the illusory wonderland with free services, while their data becomes the subject to monetizing by a genie called big data. In other words, users pay with their personal data but the price is in a way invisible. Poor means to observe and to assess the consequences of data disclosure causes hindrance for the user to be aware of and to take preventive measures.

Mobile operating systems use permission-based access control mechanism to guard system resources and sensors. Depending on the type, apps require explicit consent from the user in order to avail access to those permissions. Nonetheless, it does not put any constraint on access frequency. Granted privileges allow apps to access to users' personal information for indefinite period of time until being revoked explicitly. Available control tools lack monitoring facility which undermines the performance of access control model. It has the ability to create privacy risks and nontransparent handling of personal information for the data subject.

This thesis argues that app behavior analysis yields information which has the potential to increase transparency, to enhance privacy protection, to raise awareness regarding consequences of data disclosure, and to assist the user in informed decision making while selecting apps or services. It introduces models and methods, and demonstrates the risks with experiment results. It also takes the risks into account and makes an effort to determine apps' privacy-friendliness based on empirical data from app-behavior analysis.

Abstract [en]

Today's phone could be described as a charismatic tool that has the ability to keep human beings captivated for a considerable amount of their precious time. Users remain in the illusory wonderland with free services, while their data becomes the subject to monetizing by a genie called big data. In other words, users pay with their personal data but the price is in a way invisible. They face hindrance to be aware of and to take preventive measures because of poor means to observe and to assess consequences of data disclosure. Available control tools lack monitoring properties that do not allow the user to comprehend the magnitude of personal data access. Such circumstances can create privacy risks, erode intervenability of access control mechanism and lead to opaque handling of personal information for the data subject.

This thesis argues that app behavior analysis yields information which has the potential to increase transparency, to enhance privacy protection, to raise awareness regarding consequences of data disclosure, and to assist the user in informed decision making while selecting apps or services. It introduces models and methods, and demonstrates the data disclosure risks with experimental results. It also takes the risks into account and makes an effort to determine apps' privacy-friendliness based on empirical data from app-behavior analysis.

sted, utgiver, år, opplag, sider
Karlstad: Karlstads universitet, 2018. s. 27
Serie
Karlstad University Studies, ISSN 1403-8099 ; 2018:31
Emneord
Mobile OS, Apps, User data, Transparency, Privacy
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-68569 (URN)978-91-7063-864-0 (ISBN)978-91-7063-959-3 (ISBN)
Presentation
2018-09-07, 1D 222, Universitetsgatan 2, Karlstad, 10:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-08-17 Laget: 2018-07-23 Sist oppdatert: 2019-07-11bibliografisk kontrollert

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