Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Multilateral Privacy Impact Analysis Method for Android Apps
Goethe University Frankfurt, Germany.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Prisec, Privacy and Security)ORCID iD: 0000-0002-5235-5335
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (Prisec, Privacy and Security)ORCID iD: 0000-0002-0418-4121
Goethe University Frankfurt, Germany.
2019 (English)In: Privacy Technologies and Policy / [ed] M. Naldi, G. F. Italiano, K. Rannenberg, M. Medina & A. Bourka, Cham: Springer, 2019, Vol. 11498, p. 87-106Conference paper, Published paper (Refereed)
Abstract [en]

Smartphone apps have the power to monitor most of people’s private lives. Apps can permeate private spaces, access and map social relationships, monitor whereabouts and chart people’s activities in digital and/or real world. We are therefore interested in how much information a particular app can and intends to retrieve in a smartphone. Privacy-friendliness of smartphone apps is typically measured based on single-source analyses, which in turn, does not provide a comprehensive measurement regarding the actual privacy risks of apps. This paper presents a multi-source method for privacy analysis and data extraction transparency of Android apps. We describe how we generate several data sets derived from privacy policies, app manifestos, user reviews and actual app profiling at run time. To evaluate our method, we present results from a case study carried out on ten popular fitness and exercise apps. Our results revealed interesting differences concerning the potential privacy impact of apps, with some of the apps in the test set violating critical privacy principles. The result of the case study shows large differences that can help make relevant app choices.

Place, publisher, year, edition, pages
Cham: Springer, 2019. Vol. 11498, p. 87-106
Series
Lecture Notes in Computer Science, LNCS, ISSN 0302-9743, E-ISSN 1611-3349 ; 11498
Keywords [en]
Smartphone apps, Case study, Security, Privacy, Android, Privacy policy, Reviews, Privacy impact, Privacy score and ranking, Privacy risk, Transparency
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-72432DOI: 10.1007/978-3-030-21752-5_7ISI: 000561013800007Scopus ID: 2-s2.0-85067825202ISBN: 978-3-030-21751-8 (print)ISBN: 978-3-030-21752-5 (electronic)OAI: oai:DiVA.org:kau-72432DiVA, id: diva2:1323331
Conference
Annual Privacy Forum 2019, Rome, Italy, June 13-14
Projects
Excellenta miljön, 8730Alert, 5617Privacy & Us, 4961Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2020-09-24Bibliographically approved
In thesis
1. Measuring Apps' Privacy-Friendliness: Introducing transparency to apps' data access behavior
Open this publication in new window or tab >>Measuring Apps' Privacy-Friendliness: Introducing transparency to apps' data access behavior
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mobile apps brought unprecedented convenience to everyday life, and nowadays, hardly any interactive service exists without having an interface through an app. The rich functionalities of apps rely on the pervasive capabilities of the mobile device, such as its cameras and other types of sensors. Consequently, apps generate a diverse and large amount of data, which can often be deemed as privacy-sensitive data. As the mobile device is also equipped with several means to transmit the collected data, such as WiFi and 4G, it brings further concerns about individuals' privacy.

Even though mobile operating systems use access control mechanisms to guard system resources and sensors, apps exercise their granted privileges in an opaque manner. Depending on the type of privilege, apps require explicit approval from the user in order to acquire access to them through permissions. Nonetheless, granting permission does not put constraints on the access frequency. Granted privileges allow the app to access users' personal data for a long period of time, typically until the user explicitly revokes the access. Furthermore, available control tools lack monitoring features, and therefore, the user faces hindrances to comprehend the magnitude of personal data access. Such circumstances can erode intervenability from the interface of the phone, lead to incomprehensible handling of personal data, and thus, create privacy risks for the user.

This thesis covers a long-term investigation of apps' data access behavior and makes an effort to shed light on various privacy implications. It also shows that app behavior analysis yields information that 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. We introduce models, methods, and demonstrate the data disclosure risks with experimental results. Finally, we show how to communicate privacy risks through the user interface by taking the results of app behavior analyses into account.

Abstract [en]

Mobile apps brought unprecedented convenience to everyday life, and nowadays, hardly any interactive service exists without having an interface through an app. The rich functionalities of apps rely on the pervasive capabilities of the mobile device. Consequently, apps generate a diverse and large amount of data, which can often be deemed as privacy-sensitive data.

Even though mobile operating systems use access control mechanisms to guard system resources and sensors, apps exercise their granted privileges in an opaque manner. Furthermore, available control tools lack monitoring features, and therefore, the user faces hindrances to comprehend the magnitude of personal data access.

This thesis covers a long-term investigation of apps' data access behavior and makes an effort to shed light on various privacy implications. It also shows that app behavior analysis yields information that 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.

Place, publisher, year, edition, pages
Karlstads universitet, 2020. p. 218
Series
Karlstad University Studies, ISSN 1403-8099 ; 2020:24
Keywords
Mobile Apps, User data, Transparency, Privacy, Data protection
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-79308 (URN)978-91-7867-132-8 (ISBN)978-91-7867-137-3 (ISBN)
Public defence
2020-10-09, 9C203, Universitetsgatan 2, Karlstad, 09:15 (English)
Opponent
Supervisors
Available from: 2020-09-09 Created: 2020-08-11 Last updated: 2020-09-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Momen, NurulFritsch, Lothar

Search in DiVA

By author/editor
Momen, NurulFritsch, Lothar
By organisation
Department of Mathematics and Computer Science (from 2013)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 10872 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf