research-article

Understanding the Purpose of Permission Use in Mobile Apps

Authors:

Yuvraj Agarwal,

Jason I. HongAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 35, Issue 4

Article No.: 43, Pages 1 - 40

https://doi.org/10.1145/3086677

Published: 11 July 2017 Publication History

Abstract

Mobile apps frequently request access to sensitive data, such as location and contacts. Understanding the purpose of why sensitive data is accessed could help improve privacy as well as enable new kinds of access control. In this article, we propose a text mining based method to infer the purpose of sensitive data access by Android apps. The key idea we propose is to extract multiple features from app code and then use those features to train a machine learning classifier for purpose inference. We present the design, implementation, and evaluation of two complementary approaches to infer the purpose of permission use, first using purely static analysis, and then using primarily dynamic analysis. We also discuss the pros and cons of both approaches and the trade-offs involved.

References

[1]

Yuvraj Agarwal and Malcolm Hall. 2013. ProtectMyPrivacy: Detecting and mitigating privacy leaks on ios devices using crowdsourcing. In Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services. 97--110.

Digital Library

[2]

Hazim Almuhimedi, Florian Schaub, Norman Sadeh, Idris Adjerid, Alessandro Acquisti, Joshua Gluck, Lorrie Faith Cranor, and Yuvraj Agarwal. 2015. Your location has been shared 5,398 times&excl;: A field study on mobile app privacy nudging. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’15). 787--796.

Digital Library

[3]

Shahriyar Amini, Jialiu Lin, Jason I. Hong, Janne Lindqvist, and Joy Zhang. 2013. Mobile application evaluation using automation and crowdsourcing. In Proceedings of the PETools.

[4]

Apktool 2016. Apktool: A tool for reverse engineering Android apk files. Retrieved from https://code.google.com/p/android-apktool/.

[5]

AppStore 2016. Wikipedia App Store (iOS). Retrieved from https://en.wikipedia.org/wiki/App_Store_(iOS).

[6]

Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel. 2014. FlowDroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’14). 259--269.

Digital Library

[7]

Kathy Wain Yee Au, Yi Fan Zhou, Zhen Huang, and David Lie. 2012. PScout: Analyzing the android permission specification. In Proceedings of the 2012 ACM Conference on Computer and Communications Security (CCS’12). 217--228.

Digital Library

[8]

Michael Backes, Sven Bugiel, Sebastian Gerling, and Philipp von Styp-Rekowsky. 2014. Android security framework: Extensible multi-layered access control on android. In Proceedings of the 30th Annual Computer Security Applications Conference (ACSAC’14). 46--55.

Digital Library

[9]

Michael Backes, Sebastian Gerling, Christian Hammer, Matteo Maffei, and Philipp von Styp-Rekowsky. 2013. AppGuard: Enforcing user requirements on android apps. In Proceedings of the 19th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS’13). 543--548.

Digital Library

[10]

Rebecca Balebako, Jaeyeon Jung, Wei Lu, Lorrie Faith Cranor, and Carolyn Nguyen. 2013. “Little brothers watching you”: Raising awareness of data leaks on smartphones. In Proceedings of the 9th Symposium on Usable Privacy and Security (SOUPS’13). 12:1--12:11.

Digital Library

[11]

Alastair R. Beresford, Andrew Rice, Nicholas Skehin, and Ripduman Sohan. 2011. MockDroid: Trading privacy for application functionality on smartphones. In Proceedings of the 12th Workshop on Mobile Computing Systems and Applications (HotMobile’11). 49--54.

Digital Library

[12]

Benjamin Bichsel, Veselin Raychev, Petar Tsankov, and Martin Vechev. 2016. Statistical deobfuscation of android applications. In CCS’16.

Digital Library

[13]

Sven Bugiel, Stephan Heuser, and Ahmad-Reza Sadeghi. 2013. Flexible and fine-grained mandatory access control on android for diverse security and privacy policies. In Proceedings of the 22nd USENIX Conference on Security (SEC’13). 131--146.

Digital Library

[14]

C4.5 2016. Wikipedia. C4.5 Algorithm. (2016). http://en.wikipedia.org/wiki/C4.5_algorithm.

[15]

CaffeineMark 2016. CaffeineMark. Retrieved from https://play.google.com/store/apps/details?id=com.android.cm38hl=zh_CN.

[16]

Erika Chin, Adrienne Porter Felt, Vyas Sekar, and David Wagner. 2012. Measuring user confidence in smartphone security and privacy. In Proceedings of the 8th Symposium on Usable Privacy and Security (SOUPS’12).

Digital Library

[17]

Cross-Validation 2016. Wikipedia. Cross-validation. Retrieved from https://en.wikipedia.org/wiki/Cross-validation_(statistics).

[18]

Benjamin Davis and Hao Chen. 2013. RetroSkeleton: Retrofitting android apps. In Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys’13).

Digital Library

[19]

Benjamin Davis, Ben Sanders, Armen Khodaverdian, and Hao Chen. 2012. I-arm-droid: A rewriting framework for in-app reference monitors for android applications. In Proceedings of the Mobile Security Technologies.

[20]

Dex2jar 2016. dex2jar. Retrieved from https://code.google.com/p/dex2jar/.

[21]

Serge Egelman, Adrienne Porter Felt, and David Wagner. 2012. Choice architecture and smartphone privacy: There’s a price for that. In Proceedings of the Workshop on the Economics of Information Security (WEIS).

[22]

William Enck, Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, and Anmol N. Sheth. 2010. TaintDroid: An information-flow tracking system for realtime privacy monitoring on smartphones. In Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI’10).

Digital Library

[23]

William Enck, Damien Octeau, Patrick McDaniel, and Swarat Chaudhuri. 2011. A study of android application security. In Proceedings of the 20th USENIX Conference on Security (SEC’11).

Digital Library

[24]

William Enck, Machigar Ongtang, and Patrick McDaniel. 2009. On lightweight mobile phone application certification. In Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS’09). 235--245.

Digital Library

[25]

Adrienne Porter Felt, Elizabeth Ha, Serge Egelman, Ariel Haney, Erika Chin, and David Wagner. 2012. Android permissions: User attention, comprehension, and behavior. In Proceedings of the 8th Symposium on Usable Privacy and Security (SOUPS’12). 3:1--3:14.

Digital Library

[26]

Peter Gilbert, Byung-Gon Chun, Landon P. Cox, and Jaeyeon Jung. 2011. Vision: Automated security validation of mobile apps at app markets. In Proceedings of the 2nd International Workshop on Mobile Cloud Computing and Services (MCS’11). 21--26.

Digital Library

[27]

GooglePlay 2016. Wikipedia. Google Play. Retrieved from http://en.wikipedia.org/wiki/Google_Play.

[28]

Michael I. Gordon, Deokhwan Kim, Jeff Perkins, Limei Gilham, Nguyen Nguyen, and Martin Rinard. 2015. Information flow analysis of android applications in DroidSafe. In Proceedings of NDSS 2015.

[29]

Alessandra Gorla, Ilaria Tavecchia, Florian Gross, and Andreas Zeller. 2014. Checking app behavior against app descriptions. In Proceedings of the 36th International Conference on Software Engineering (ICSE’14). 1025--1035.

Digital Library

[30]

Marian Harbach, Markus Hettig, Susanne Weber, and Matthew Smith. 2014. Using personal examples to improve risk communication for security and privacy decisions. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI’14).

Digital Library

[31]

Stephan Heuser, Adwait Nadkarni, William Enck, and Ahmad-Reza Sadeghi. 2014. ASM: A programmable interface for extending android security. In Proceedigns of the 23rd USENIX Security Symposium (USENIX Security’14). 1005--1019.

Digital Library

[32]

Peter Hornyack, Seungyeop Han, Jaeyeon Jung, Stuart Schechter, and David Wetherall. 2011. These aren’t the droids you’re looking for: Retrofitting android to protect data from imperious applications. In Proceedings of the 18th ACM Conference on Computer and Communications Security (CCS’11). 639--652.

Digital Library

[33]

Cuixiong Hu and Iulian Neamtiu. 2011. Automating GUI testing for android applications. In Proceedings of the 6th International Workshop on Automation of Software Test. 77--83.

Digital Library

[34]

Wei Huang, Yao Dong, Ana Milanova, and Julian Dolby. 2015. Scalable and precise taint analysis for android. In Proceedings of the 2015 International Symposium on Software Testing and Analysis (ISSTA’15). 106--117.

Digital Library

[35]

Qatrunnada Ismail, Tousif Ahmed, Apu Kapadia, and Michael Reiter. 2015. Crowdsourced exploration of security configurations. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’15).

Digital Library

[36]

JD-Core-Java 2016. JD-Core-Java. Retrieved from http://jd.benow.ca/.

[37]

Casper S. Jensen, Mukul R. Prasad, and Anders Møller. 2013. Automated testing with targeted event sequence generation. In Proceedings of ISSTA’13. 67--77.

Digital Library

[38]

Yiming Jing, Gail-Joon Ahn, Ziming Zhao, and Hongxin Hu. 2014. RiskMon: Continuous and automated risk assessment of mobile applications. In Proceedings of the 4th ACM Conference on Data and Application Security and Privacy (CODASPY’14). 99--110.

Digital Library

[39]

Jaeyeon Jung, Seungyeop Han, and David Wetherall. 2012. Short paper: Enhancing mobile application permissions with runtime feedback and constraints. In Proceedings of the 2nd ACM Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM’12). 45--50.

Digital Library

[40]

Patrick Gage Kelley, Lorrie Faith Cranor, and Norman Sadeh. 2013. Privacy as part of the app decision-making process. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’13). 3393--3402.

Digital Library

[41]

LibRadar 2016. LibRadar: Detecting Libraries in Android Apps. Retrieved from http://radar.pkuos.org/. (2016).

[42]

LibSVM 2016. LIBSVM—A Library for Support Vector Machines. Retrieved from https://www.csie.ntu.edu.tw/ cjlin/libsvm/.

[43]

Jialiu Lin, Shahriyar Amini, Jason I. Hong, Norman Sadeh, Janne Lindqvist, and Joy Zhang. 2012. Expectation and purpose: Understanding users’ mental models of mobile app privacy through crowdsourcing. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp’12). 501--510.

Digital Library

[44]

Jialiu Lin, Bin Liu, Norman Sadeh, and Jason I. Hong. 2014. Modeling users’ mobile app privacy preferences: Restoring usability in a sea of permission settings. In Proceedings of the 2014 Symposium On Usable Privacy and Security (SOUPS’14).

[45]

Mario Linares-Vásquez, Andrew Holtzhauer, Carlos Bernal-Cárdenas, and Denys Poshyvanyk. 2014. Revisiting android reuse studies in the context of code obfuscation and library usages. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR’14). 242--251.

Digital Library

[46]

Bin Liu, Bin Liu, Hongxia Jin, and Ramesh View. 2015. Efficient privilege de-escalation for ad libraries in mobile apps. In Proceedings of the the 13th International Conference on Mobile Systems, Applications, and Services (MobiSys’15).

Digital Library

[47]

Looper 2016. Looper. Retrieved from http://developer.android.com/reference/android/os/Looper.html.

[48]

Ziang Ma, Haoyu Wang, Yao Guo, and Xiangqun Chen. 2016. LibRadar: Fast and accurate detection of third-party libraries in android apps. In Proceedings of the 2016 IEEE/ACM 38th IEEE International Conference on Software Engineering Companion. 653--656.

Digital Library

[49]

Mallet 2016. Mallet: MAchine Learning for LanguagE ToolkiT. Retrieved from http://mallet.cs.umass.edu/.

[50]

Clara Mancini, Keerthi Thomas, Yvonne Rogers, Blaine A. Price, Lukazs Jedrzejczyk, Arosha K. Bandara, Adam N. Joinson, and Bashar Nuseibeh. 2009. From spaces to places: Emerging contexts in mobile privacy. In Proceedings of the 11th International Conference on Ubiquitous Computing (UbiComp’09). 1--10.

Digital Library

[51]

Maximum Entropy 2016. Wikipedia Maximum Entropy. Retrieved from http://en.wikipedia.org/wiki/Maximum_entropy.

[52]

Monkey 2016. UI/Application Exerciser Monkey. Retrieved from developer.android.com/tools/help/monkey.html.

[53]

MultipleThreads 2016. MultipleThreads. Retrieved from http://developer.android.com/intl/en-us/training/multiple-threads/index.html.

[54]

Mohammad Nauman, Sohail Khan, and Xinwen Zhang. 2010. Apex: Extending android permission model and enforcement with user-defined runtime constraints. In Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security (ASIACCS’10). 328--332.

Digital Library

[55]

Machigar Ongtang, Stephen McLaughlin, William Enck, and Patrick McDaniel. 2009. Semantically rich application-centric security in android. In Proceedings of the 2009 Annual Computer Security Applications Conference (ACSAC’09). 340--349.

Digital Library

[56]

Rahul Pandita, Xusheng Xiao, Wei Yang, William Enck, and Tao Xie. 2013. WHYPER: Towards automating risk assessment of mobile applications. In Proceedings of the 22nd USENIX Conference on Security (SEC’13). 527--542.

Digital Library

[57]

Paul Pearce, Adrienne Porter Felt, Gabriel Nunez, and David Wagner. 2012. AdDroid: Privilege separation for applications and advertisers in android. In Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security (ASIACCS’12).

Digital Library

[58]

PermissionMappings 2015. Permission mappings. Retrieved from http://pscout.csl.toronto.edu/.

[59]

Porter 2015. The Porter Stemming Algorithm. Retrieved from http://tartarus.org/martin/PorterStemmer/.

[60]

PrivacyGrade 2015. PrivacyGrade: Grading the privacy of smartphone apps. Retrieved from http://privacygrade.org/.

[61]

PScout API 2015. Documented API calls mappings. Retrieved from http://pscout.csl.toronto.edu/download.php?file=results/jellybean_publishedapimapping.

[62]

PScout ContentProvider 2015. Content Provider (URI strings) with permissions. Retrieved from http://pscout.csl.toronto.edu/download.php?file=results/jellybean_contentproviderpermission.

[63]

PScout Intent 2015. Intents with Permissions. Retrieved from http://pscout.csl.toronto.edu/download.php?file=results/jellybean_intentpermissions.

[64]

Zhengyang Qu, Vaibhav Rastogi, Xinyi Zhang, Yan Chen, Tiantian Zhu, and Zhong Chen. 2014. AutoCog: Measuring the description-to-permission fidelity in android applications. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS’14). 1354--1365.

Digital Library

[65]

Franziska Roesner and Tadayoshi Kohno. 2013. Securing embedded user interfaces: Android and beyond. In Proceedings of the 22nd USENIX Conference on Security (SEC’13). 97--112.

Digital Library

[66]

Golam Sarwar, Olivier Mehani, Roksana Boreli, and Dali Kaafar. 2013. On the effectiveness of dynamic taint analysis for protecting against private information leaks on android-based devices. In Proceedings of the 10th International Conference on Security and Cryptography (SECRYPT’13). 461--467.

[67]

Daniel Schreckling, Johannes Kstler, and Matthias Schaff. 2013. Kynoid: Real-time enforcement of fine-grained, user-defined, and data-centric security policies for Android. Information Security Technical Report 17, 3 (2013), 71--80.

Digital Library

[68]

SciKit 2016. Scikit-learn Machine learning in Python. Retrieved from http://scikit-learn.org/stable/index.html.

[69]

Shashi Shekhar, Michael Dietz, and Dan S. Wallach. 2012. AdSplit: Separating smartphone advertising from applications. In Proceedings of the 21st USENIX Conference on Security Symposium (Security’12).

Digital Library

[70]

Fuming Shih, Ilaria Liccardi, and Daniel Weitzner. 2015. Privacy tipping points in smartphones privacy preferences. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’15). 807--816.

Digital Library

[71]

Irina Shklovski, Scott D. Mainwaring, Halla Hrund Skúladóttir, and Höskuldur Borgthorsson. 2014. Leakiness and creepiness in app space: Perceptions of privacy and mobile app use. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI’14). 2347--2356.

Digital Library

[72]

Ryan Stevens, Clint Gibler, Jon Crussell, Jeremy Erickson, and Hao Chen. 2012. Investigating user privacy in android ad libraries. In Proceedings of the Workshop on Mobile Security Technologies (MoST).

[73]

StringMatching 2016. Wikipedia Approximate String Matching. Retrieved from http://en.wikipedia.org/wiki/Approximate_string_matching.

[74]

SVM 2016. Wikipedia Support Vector Machine. Retrieved from http://en.wikipedia.org/wiki/Support_vector_machine.

[75]

Yang Tang, Phillip Ames, Sravan Bhamidipati, Ashish Bijlani, Roxana Geambasu, and Nikhil Sarda. 2012. CleanOS: Limiting mobile data exposure with idle eviction. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI’12). 77--91.

Digital Library

[76]

Eran Toch, Justin Cranshaw, Paul Hankes Drielsma, Janice Y. Tsai, Patrick Gage Kelley, James Springfield, Lorrie Cranor, Jason Hong, and Norman Sadeh. 2010. Empirical models of privacy in location sharing. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing (UbiComp’10). 129--138.

Digital Library

[77]

Omer Tripp and Julia Rubin. 2014. A Bayesian approach to privacy enforcement in smartphones. In Proceedings of the 23rd USENIX Conference on Security Symposium (Security’14).

Digital Library

[78]

Haoyu Wang, Yao Guo, Ziang Ma, and Xiangqun Chen. 2015a. WuKong: A scalable and accurate two-phase approach to android app clone detection. In Proceedings of the ACM International Symposium on Software Testing and Analysis (ISSTA’15). 71--82.

Digital Library

[79]

Haoyu Wang, Yao Guo, Zihao Tang, Guangdong Bai, and Xiangqun Chen. 2015b. Reevaluating android permission gaps with static and dynamic analysis. In Proceedings of GLOBECOM’15.

[80]

Haoyu Wang, Jason I. Hong, and Yao Guo. 2015c. Using text mining to infer the purpose of permission use in mobile apps. In The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp’15). 1107--1118.

Digital Library

[81]

Haoyu Wang, Zhe Liu, Yao Guo, Xiangqun Chen, Miao Zhang, Guoai Xu, and Jason Hong. 2017. An explorative study of the mobile app ecosystem from app developers’ perspective. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 163--172.

Digital Library

[82]

Jiayu Wang and Qigeng Chen. 2014. ASPG: Generating android semantic permissions. In Proceedings of the IEEE 17th International Conference on Computational Science and Engineering. 591--598.

Digital Library

[83]

Takuya Watanabe, Mitsuaki Akiyama, Tetsuya Sakai, and Tatsuya Mori. 2015. Understanding the inconsistencies between text descriptions and the use of privacy-sensitive resources of mobile apps. In 11th Symposium On Usable Privacy and Security (SOUPS 2015). 241--255.

[84]

WordList 2015. English wordlist. (2015). http://www-personal.umich.edu/jlawler/wordlist.

[85]

Rubin Xu, Hassen Saïdi, and Ross Anderson. 2012. Aurasium: Practical policy enforcement for android applications. In Proceedings of the 21st USENIX Conference on Security Symposium (Security’12).

Digital Library

[86]

Wei Yang, Xusheng Xiao, Benjamin Andow, Sihan Li, Tao Xie, and William Enck. 2015. AppContext: Differentiating malicious and benign mobile app behaviors using context. In Proceedings of the 37th International Conference on Software Engineering (ICSE’15). 303--313.

Digital Library

[87]

Zhemin Yang, Min Yang, Yuan Zhang, Guofei Gu, Peng Ning, and X. Sean Wang. 2013. AppIntent: Analyzing sensitive data transmission in android for privacy leakage detection. In Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security (CCS’13). 1043--1054.

Digital Library

[88]

Yajin Zhou, Xinwen Zhang, Xuxian Jiang, and Vincent W Freeh. 2011. Taming information-stealing smartphone applications (on android). In Proceedings of the 4th International Conference on Trust and Trustworthy Computing (TRUST’11). 93--107.

Digital Library

Cited By

Makhdoom IAbolhasan MLipman JShariati NFranklin DPiccardi M(2024)Securing Personally Identifiable Information: A Survey of SOTA Techniques, and a Way ForwardIEEE Access10.1109/ACCESS.2024.344701712(116740-116770)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3447017
Wang RWang SYan HWang XWilliams BChen YNeville J(2023)WSiPProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25592(4685-4692)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25592
Sarker JTurzo ADong MBosu A(2023)Automated Identification of Toxic Code Reviews Using ToxiCRACM Transactions on Software Engineering and Methodology10.1145/358356232:5(1-32)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3583562
Show More Cited By

Index Terms

Understanding the Purpose of Permission Use in Mobile Apps

Recommendations

Using text mining to infer the purpose of permission use in mobile apps
UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Understanding the purpose of why sensitive data is used could help improve privacy as well as enable new kinds of access control. In this paper, we introduce a new technique for inferring the purpose of sensitive data usage in the context of Android ...
Messing with Android's Permission Model
TRUSTCOM '12: Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications

Permission models have become very common on smartphone operating systems to control the rights granted to installed third party applications (apps). Prior to installing an app, the user is typically presented with a dialog box showing the permissions ...
SecuRank: Starving Permission-Hungry Apps Using Contextual Permission Analysis
SPSM '16: Proceedings of the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices

Competition among app developers has caused app stores to be permeated with many groups of general-purpose apps that are functionally-similar. Examples are the many flashlight or alarm clock apps to choose from. Within groups of functionally-similar ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 35, Issue 4

Special issue: Search, Mining and their Applications on Mobile Devices

October 2017

461 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3112649

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2017

Accepted: 01 April 2017

Revised: 01 March 2017

Received: 01 July 2016

Published in TOIS Volume 35, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
944
Total Downloads

Downloads (Last 12 months)52
Downloads (Last 6 weeks)9

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Makhdoom IAbolhasan MLipman JShariati NFranklin DPiccardi M(2024)Securing Personally Identifiable Information: A Survey of SOTA Techniques, and a Way ForwardIEEE Access10.1109/ACCESS.2024.344701712(116740-116770)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3447017
Wang RWang SYan HWang XWilliams BChen YNeville J(2023)WSiPProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25592(4685-4692)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i4.25592
Sarker JTurzo ADong MBosu A(2023)Automated Identification of Toxic Code Reviews Using ToxiCRACM Transactions on Software Engineering and Methodology10.1145/358356232:5(1-32)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1145/3583562
Chen ZZhang JSarro FHarman M(2023)A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning ClassifiersACM Transactions on Software Engineering and Methodology10.1145/358356132:4(1-30)Online publication date: 27-May-2023
https://dl.acm.org/doi/10.1145/3583561
Hou NXia XZheng Y(2023)Jamming of LoRa PHY and CountermeasureACM Transactions on Sensor Networks10.1145/358313719:4(1-27)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3583137
Liberatore P(2023)Mixed Iterated Revisions: Rationale, Algorithms, and ComplexityACM Transactions on Computational Logic10.1145/358307124:3(1-49)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3583071
McGregor MAzzopardi LHalvey M(2023)A Systematic Review of Cost, Effort, and Load Research in Information Search and Retrieval, 1972–2020ACM Transactions on Information Systems10.1145/358306942:1(1-39)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3583069
Zhang ZKlees GWang EHicks MWei S(2023)Fuzzing Configurations of Program OptionsACM Transactions on Software Engineering and Methodology10.1145/358059732:2(1-21)Online publication date: 29-Mar-2023
https://dl.acm.org/doi/10.1145/3580597
Yitagesu SXing ZZhang XFeng ZLi XHan L(2023)Extraction of Phrase-based Concepts in Vulnerability Descriptions through Unsupervised LabelingACM Transactions on Software Engineering and Methodology10.1145/357963832:5(1-45)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3579638
Raymond JStevanovic RBernoudy WBoothby KMcGeoch CBerkley AFarré PPasvolsky JKing A(2023)Hybrid Quantum Annealing for Larger-than-QPU Lattice-structured ProblemsACM Transactions on Quantum Computing10.1145/35793684:3(1-30)Online publication date: 8-Apr-2023
https://dl.acm.org/doi/10.1145/3579368
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents