Android permission recommendation using transitive Bayesian inference model

Bahman Rashidi; Carol Fung; Anh Nguyen; Tam Vu

doi:10.1007/978-3-319-45744-4_24

Android permission recommendation using transitive Bayesian inference model

Bahman Rashidi, Carol Fung, Anh Nguyen, Tam Vu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.

Original language	English
Title of host publication	Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings
Editors	Sokratis Katsikas, Catherine Meadows, Ioannis Askoxylakis, Sotiris Ioannidis
Publisher	Springer Verlag
Pages	477-497
Number of pages	21
ISBN (Print)	9783319457437
DOIs	https://doi.org/10.1007/978-3-319-45744-4_24
State	Published - 2016
Event	21st European Symposium on Research in Computer Security, ESORICS 2016 - Heraklion, Greece Duration: Sep 26 2016 → Sep 30 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9878 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st European Symposium on Research in Computer Security, ESORICS 2016
Country/Territory	Greece
City	Heraklion
Period	09/26/16 → 09/30/16

Access to Document

10.1007/978-3-319-45744-4_24

Cite this

Rashidi, B., Fung, C., Nguyen, A., & Vu, T. (2016). Android permission recommendation using transitive Bayesian inference model. In S. Katsikas, C. Meadows, I. Askoxylakis, & S. Ioannidis (Eds.), Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings (pp. 477-497). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9878 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-45744-4_24

Rashidi, Bahman ; Fung, Carol ; Nguyen, Anh et al. / Android permission recommendation using transitive Bayesian inference model. Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings. editor / Sokratis Katsikas ; Catherine Meadows ; Ioannis Askoxylakis ; Sotiris Ioannidis. Springer Verlag, 2016. pp. 477-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.",

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Rashidi, B, Fung, C, Nguyen, A & Vu, T 2016, Android permission recommendation using transitive Bayesian inference model. in S Katsikas, C Meadows, I Askoxylakis & S Ioannidis (eds), Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9878 LNCS, Springer Verlag, pp. 477-497, 21st European Symposium on Research in Computer Security, ESORICS 2016, Heraklion, Greece, 09/26/16. https://doi.org/10.1007/978-3-319-45744-4_24

Android permission recommendation using transitive Bayesian inference model. / Rashidi, Bahman; Fung, Carol; Nguyen, Anh et al.
Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings. ed. / Sokratis Katsikas; Catherine Meadows; Ioannis Askoxylakis; Sotiris Ioannidis. Springer Verlag, 2016. p. 477-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9878 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Vu, Tam

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PY - 2016

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N2 - In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.

AB - In current Android architecture, users have to decide whether an app is safe to use or not. Technical-savvy users can make correct decisions to avoid unnecessary privacy breach. However, most users may have difficulty to make correct decisions. DroidNet is an Android permission recommendation framework based on crowdsourcing. In this framework, DroidNet runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or reject the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using transitional Bayesian inference model. The recommendation is based on the aggregated expert responses and its confidence level. Our evaluation results demonstrate that given sufficient number of experts in the network, DroidNet can provide accurate recommendations and cover majority of app requests given a small coverage from a small set of initial experts.

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Rashidi B, Fung C, Nguyen A, Vu T. Android permission recommendation using transitive Bayesian inference model. In Katsikas S, Meadows C, Askoxylakis I, Ioannidis S, editors, Computer Security - 21st European Symposium on Research in Computer Security, ESORICS 2016, Proceedings. Springer Verlag. 2016. p. 477-497. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-45744-4_24

Android permission recommendation using transitive Bayesian inference model

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