POWERFUL

Mobile app fingerprinting via power analysis

Yimin Chen, Xiaocong Jin, Jingchao Sun, Rui Zhang, Yanchao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.

Original languageEnglish (US)
Title of host publicationINFOCOM 2017 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509053360
DOIs
StatePublished - Oct 2 2017
Event2017 IEEE Conference on Computer Communications, INFOCOM 2017 - Atlanta, United States
Duration: May 1 2017May 4 2017

Other

Other2017 IEEE Conference on Computer Communications, INFOCOM 2017
CountryUnited States
CityAtlanta
Period5/1/175/4/17

Fingerprint

Application programs
Electric power utilization
Experiments

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Chen, Y., Jin, X., Sun, J., Zhang, R., & Zhang, Y. (2017). POWERFUL: Mobile app fingerprinting via power analysis. In INFOCOM 2017 - IEEE Conference on Computer Communications [8057232] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2017.8057232

POWERFUL : Mobile app fingerprinting via power analysis. / Chen, Yimin; Jin, Xiaocong; Sun, Jingchao; Zhang, Rui; Zhang, Yanchao.

INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2017. 8057232.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chen, Y, Jin, X, Sun, J, Zhang, R & Zhang, Y 2017, POWERFUL: Mobile app fingerprinting via power analysis. in INFOCOM 2017 - IEEE Conference on Computer Communications., 8057232, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Conference on Computer Communications, INFOCOM 2017, Atlanta, United States, 5/1/17. https://doi.org/10.1109/INFOCOM.2017.8057232
Chen Y, Jin X, Sun J, Zhang R, Zhang Y. POWERFUL: Mobile app fingerprinting via power analysis. In INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc. 2017. 8057232 https://doi.org/10.1109/INFOCOM.2017.8057232
Chen, Yimin ; Jin, Xiaocong ; Sun, Jingchao ; Zhang, Rui ; Zhang, Yanchao. / POWERFUL : Mobile app fingerprinting via power analysis. INFOCOM 2017 - IEEE Conference on Computer Communications. Institute of Electrical and Electronics Engineers Inc., 2017.
@inproceedings{f35505a7cfe64900afb6ea179adc088a,
title = "POWERFUL: Mobile app fingerprinting via power analysis",
abstract = "Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9{\%}, demonstrating the feasibility of POWERFUL.",
author = "Yimin Chen and Xiaocong Jin and Jingchao Sun and Rui Zhang and Yanchao Zhang",
year = "2017",
month = "10",
day = "2",
doi = "10.1109/INFOCOM.2017.8057232",
language = "English (US)",
booktitle = "INFOCOM 2017 - IEEE Conference on Computer Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - POWERFUL

T2 - Mobile app fingerprinting via power analysis

AU - Chen, Yimin

AU - Jin, Xiaocong

AU - Sun, Jingchao

AU - Zhang, Rui

AU - Zhang, Yanchao

PY - 2017/10/2

Y1 - 2017/10/2

N2 - Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.

AB - Which apps a mobile user has and how they are used can disclose significant private information about the user. In this paper, we present the design and evaluation of POWERFUL, a new attack which can fingerprint sensitive mobile apps (or infer sensitive app usage) by analyzing the power consumption profiles on Android devices. POWERFUL works on the observation that distinct apps and their different usage patterns all lead to distinguishable power consumption profiles. Since the power profiles on Android devices require no permission to access, POWERFUL is very difficult to detect and can pose a serious threat against user privacy. Extensive experiments involving popular and sensitive apps in Google Play Store show that POWERFUL can identify the app used at any particular time with accuracy up to 92.9%, demonstrating the feasibility of POWERFUL.

UR - http://www.scopus.com/inward/record.url?scp=85034114114&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034114114&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2017.8057232

DO - 10.1109/INFOCOM.2017.8057232

M3 - Conference contribution

BT - INFOCOM 2017 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

ER -