The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Privacy as part of the app decision-making process
Abstract
Smartphones have unprecedented access to sensitive personal information. While users report having privacy concerns, they may not actively consider privacy while downloading apps from smartphone application marketplaces. Currently, Android users have only the Android permissions display, which appears after they have selected an app to download, to help them understand how applications access their information. We investigate how permissions and privacy could play a more active role in app-selection decisions. We designed a short "Privacy Facts' display, which we tested in a 20-participant lab study and a 366-participant online experiment. We found that by bringing privacy information to the user when they were making the decision and by presenting it in a clearer fashion, we could assist users in choosing applications that request fewer permissions.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This is in line with the findings of Kelley et al. (2013) who found in their study on app decision-making process that users consider factors such as cost, functionality, design, ratings, reviews and downloads as more important than requested permissions.”
From Putting the privacy paradox to the test: Online privacy and security behaviors among users with technical knowledge, privacy awareness, and financial resources (2019) · cites this paper as Kelley et al. (2013)
Citations
Cites
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
Enck, W., Gilbert, P., Chun, B.-G., Cox, L. P., Jung, J., McDaniel, P. D., & Sheth, A. N. (2010) · doi:10.5555/1924943.1924971 · Cited by 2,259
Android permissions demystified
Felt, A. P., Chin, E., Hanna, S., Song, D. X., & Wagner, D. (2011) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2046707.2046779 · Cited by 1,305
Felt, A. P., Ha, E., Egelman, S., Haney, A., Chin, E., & Wagner, D. (2012) · Proceedings of the Eighth Symposium on Usable Privacy and Security · doi:10.1145/2335356.2335360 · Cited by 983
Barrera, D., Kayacık, H. G., van Oorschot, P. C., & Somayaji, A. B. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866317 · Cited by 485
A "nutrition label" for privacy
Kelley, P. G., Bresee, J., Cranor, L. F., & Reeder, R. W. (2009) · Symposium On Usable Privacy and Security · doi:10.1145/1572532.1572538 · Cited by 483
Self-citationLin (2012) · doi:10.1145/2370216.2370290 · Cited by 451
Self-citationA Conundrum of Permissions: Installing Applications on an Android Smartphone
Kelley, P. G., Consolvo, S., Cranor, L. F., Jung, J., Sadeh, N., & Wetherall, D. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34638-5_6 · Cited by 352
Self-citationI've got 99 problems, but vibration ain't one
Felt, A. P., Egelman, S., & Wagner, D. (2012) · Security and Privacy in Smartphones and Mobile Devices · doi:10.1145/2381934.2381943 · Cited by 270
Good, N. S., Dhamija, R., Großklags, J., Thaw, D. B., Aronowitz, S., Mulligan, D. K., & Konstan, J. A. (2005) · Symposium On Usable Privacy and Security · doi:10.1145/1073001.1073006 · Cited by 133
Egelman, S., Tsai, J., Cranor, L. F., & Acquisti, A. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518752 · Cited by 120
Self-citation
Research works printed but not linked: 5
- Juniper Networks . Mobile malware development continues to rise, android leads the way , 2011 . http://globalthreatcenter.com/?p=2492. Juniper Networks. Mobile malware development continues to rise, android leads the way, 2011. http://globalthreatcenter.com/?p=2492.
- Labs , M. Mcafee threats report: Third quarter 2011 , 2011. http://www.mcafee.com/us/resources/reports/rpquarterly-threat-q3-2011.pdf. Labs, M. Mcafee threats report: Third quarter 2011, 2011. http://www.mcafee.com/us/resources/reports/rpquarterly-threat-q3-2011.pdf.
- Namestnikov , Y. It threat evolution: Q3 2011 , 2011. http://www.securelist.com/en/analysis/204792201/IT_Threat_Evolution_Q3_2011. Namestnikov, Y. It threat evolution: Q3 2011, 2011. http://www.securelist.com/en/analysis/204792201/IT_Threat_Evolution_Q3_2011.
- Rashid , F. Y. Black hat: Researchers find way to "bounce" malware into google app store , 2012 . http://www.scmagazine.com/blackhat-researchers-find-way-to-bounce-malware-intogoogle-app-store/article/252098/. Rashid, F. Y. Black hat: Researchers find way to "bounce" malware into google app store, 2012. http://www.scmagazine.com/blackhat-researchers-find-way-to-bounce-malware-intogoogle-app-store/article/252098/.
- Vidas , T. , Christin , N. , and Cranor , L. F. Curbing android permission creep. In W2 SP 2011 ( 2011 ). Vidas, T., Christin, N., and Cranor, L. F. Curbing android permission creep. In W2SP 2011 (2011).
Cited by
Sorted by citations
Items 201–300 of 313
Razavi, R., Xue, G., & Akpan, I. J. (2023) · Big Data · doi:10.1089/big.2022.0182 · Cited by 4
“Sign in with ... Privacy ”: Timely Disclosure of Privacy Differences among Web SSO Login Options
Morkonda, S. G., Chiasson, S., & van Oorschot, P. C. (2025) · ACM Transactions on Privacy and Security · doi:10.1145/3711898 · Cited by 11
"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps
Harkous, H., & Aberer, K. (2017) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.48550/arxiv.1702.08234 · Cited by 4
Autonomous Permission Recommendation
Gao, H., Guo, C., Huang, D., Hou, X., Wu, Y., Xu, J., He, Z., & Bai, G. (2020) · IEEE Access · doi:10.1109/access.2020.2967139 · Cited by 4
Akgul, O., Abu-Salma, R., Bai, W., Redmiles, E. M., Mazurek, M. L., & Ur, B. (2021) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/3463676.3485602 · Cited by 5
Using Visualizations to Enhance Users' Understanding of App Activities on Android Devices
Eze, C., Nurse, J. R. C., & Happa, J. (2016) · Kent Academic Repository (University of Kent) · doi:10.22667/jowua.2016.03.31.039 · Cited by 5
The Listening Patterns to System Events by Benign and Malicious Android Apps
Mohsen, F., & Shehab, M. (2016) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2016.083 · Cited by 4
SoK: Anatomy of Effective Cybersecurity Label DevelopmentPreprint
Caven, P. J., Gopavaram, S. R., Dev, J., & Camp, L. J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4591786 · Cited by 4
Kenderova, G., Birrell, E., & Munson, S. A. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3788682 · Cited by 3
A new approach for effective malware detection in Android-based devices
Deypir, M. (2016) · International ISC Conference on Information Security and Cryptology (ISCISC) · doi:10.1109/iscisc.2016.7736461 · Cited by 3
Wang, Z., & Chen, Z. (2023) · Bulletin of Science Technology & Society · doi:10.1177/02704676231220842 · Cited by 8
Retractedvon Kalckreuth, N., & Feufel, M. A. (2025) · Digital Health · doi:10.1177/20552076251351197 · Cited by 2
Almuhimedi, H., Schaub, F., Sadeh, N., Adjerid, I., Acquisti, A., Gluck, J., Cranor, L. F., & Agarwal, Y. (2018) · KiltHub Repository · doi:10.1184/r1/6626573 · Cited by 3
Self-citationIf You Can't Measure It, You Can't Manage It: Towards Quantification of Privacy Policies
Alohaly, M., & Takabi, H. (2016) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2016.082 · Cited by 3
Exploring Psychological Need Fulfillment for Security and Privacy Actions on Smartphones
Kraus, L., Wechsung, I., & Mller, S. (2016) · European Symposium on Usable Security (EuroUSEC) · doi:10.14722/eurousec.2016.23009 · Cited by 4
Shahidi, N., Bourliataux‐Lajoinie, S., & Tossan, V. (2022) · Management & Avenir · doi:10.3917/mav.130.0135 · Cited by 3
A comic-based approach to permission request communication
Watson, K., Just, M., & Berg, T. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102942 · Cited by 3
A Study of User Concerns about Smartphone Privacy
Fung, C., Motti, V. G., Zhang, K., & Qian, Y. (2022) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet56116.2022.9955623 · Cited by 3
Re-designing Permission Requirements to Encourage BYOD Policy Adherence
Lee, L., & Still, J. D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20376-8_33 · Cited by 4
An Investigation of Comic-Based Permission Requests
Watson, K., Just, M., & Berg, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-70852-8_15 · Cited by 3
Hassib, M. E., Abdelmoteleb, H., & Khamis, M. (2020) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3428361.3428384 · Cited by 3
Predicting Mobile App Privacy Preferences with Psychographics
McNamara, A., Verma, A., Stallings, J. W., & Staddon, J. (2016) · ACM Workshop on Privacy in the Electronic Society (WPES) · doi:10.1145/2994620.2994631 · Cited by 3
Signalling over-privileged mobile applications using passive security indicators
Iacono, L. L., Gorski, P. L., Grosse, J., & Gruschka, N. (2016) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2016.11.006 · Cited by 4
Understanding User Tradeoffs for Search in Encrypted Communication
Bai, W., Lynton, C., Papamanthou, C., & Mazurek, M. L. (2018) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2018.00026 · Cited by 3
Liu, X. M. (2018) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-5225-7128-5.ch011 · Cited by 3
Nudging the User with Privacy Indicator: A Study on the App Selection Behavior of the User
Bock, S., & Momen, N. (2020) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3419249.3420111 · Cited by 7
Analyzing Unnecessary Permissions Requested by Android Apps Based on Users’ Opinions
Kang, J.-A., Kim, D., Kim, H., & Huh, J. H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-15087-1_6 · Cited by 4
Putting Context in the Network Access of Mobile Applications Using Fuzzy Analytic Hierarchy Process
Mowafi, Y., Al-Aqarbeh, T., & Alazrai, R. (2019) · International Journal of Decision Support System Technology · doi:10.4018/ijdsst.2019040102 · Cited by 3
Are We Really Informed on the Rights GDPR Guarantees?
Sideri, M., & Gritzalis, S. (2020) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-57404-8_24 · Cited by 3
Smartphone Security Behavioral Scale: A New Psychometric Measurement for Smartphone SecurityPreprint
Huang, H., Demetriou, S., Banerjee, R., Tuncay, G. S., Gunter, C. A., & Bashir, M. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01721 · Cited by 6
Polukarov, Y., Zemlyanska, O., Prakhovnik, N., Arlamov, O., & Kyrylchuk, O. (2022) · doi:10.36074/logos-20.05.2022 · Cited by 3
Informed ShoppAR - Visualizing Privacy Information in Augmented Reality
Kaiser, J.-N., Marianski, T., Jung, F., Woźniak, M. P., & Boll, S. (2022) · Mensch und Computer (MuC) · doi:10.1145/3543758.3549884 · Cited by 2
Research on Personal Privacy Security Detection Technology for Android Application
Wang, C., Jiahan, D., Wang, X., & Ren, T. (2022) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac54830.2022.9930036 · Cited by 2
Legal-Ethical Challenges and Technological Solutions to e-Health Data Consent in the EU
Doan, X., Florea, M., & Carter, S. E. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230088 · Cited by 2
International Symposium on Technikpsychologie (TecPsy) 2023
Gerber, N., & Zimmermann, V. (2023) · Sciendo eBooks · doi:10.2478/9788366675896 · Cited by 4
Incorporating Taxonomic Reasoning and Regulatory Knowledge into Automated Privacy Question Answering
Ravichander, A., Yang, I., Chen, R., Wilson, S., Norton, T. B., & Sadeh, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0579-8_31 · Cited by 2
Self-citationIDPFilter: Mitigating interdependent privacy issues in third-party apps
Liu, S., & Biczók, G. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104321 · Cited by 2
Haque, E. U., & Khan, M. M. H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713251 · Cited by 2
A Study on User Preference: Influencing App Selection Decision with Privacy Indicator
Bock, S., & Momen, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60114-0_39 · Cited by 2
POSTER: Design Ideas for Privacy-aware User Interfaces for Mobile Devices
Tailor, N., He, Y., & Wagner, I. (2016) · Proceedings of the 9th ACM Conference on Security & Privacy in Wireless and Mobile Networks · doi:10.1145/2939918.2942420 · Cited by 2
Towards a Framework to Facilitate the Mobile Advertising Ecosystem
Chen, G., Ji, S., & Copeland, J. A. (2016) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads.2016.0068 · Cited by 2
Method for privacy requirements elicitation in ubiquitous computing
Junior, D. P. D. S., de Souza, P. C., & Maciel, C. D. O. (2018) · Brazilian Symposium on Software Engineering · doi:10.1145/3266237.3266239 · Cited by 2
What Do We Know About Our Rights to Data Protection? A Greek Case Study
Sideri, M., Fontaras, A., & Gritzalis, S. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-37545-4_2 · Cited by 2
The impact of mHealth interventions
Britt, R. K., Maddox, J., Kanthawala, S., & Hayes, J. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816958-2.00012-5 · Cited by 2
Examining Power Use and the Privacy Paradox between Intention vs. Actual Use of Mobile Applications
Namara, M., Anaraky, R. G., Wiśniewski, P., Page, X., & Knijnenburg, B. P. (2021) · European Symposium on Usable Security (EuroUSEC) · doi:10.1145/3481357.3481513 · Cited by 2
Understanding Users’ Behavior towards Applications Privacy Policies
Ullah, S., Khan, M. S., Lee, C., & Hanif, M. (2022) · Electronics · doi:10.3390/electronics11020246 · Cited by 2
Kreuder, A., Haehn, L., Klütsch, J., Schlittmeier, S. J., & Frick, U. (2023) · Mensch und Computer (MuC) · doi:10.1145/3603555.3608570 · Cited by 2
Does the Presence of Privacy Relevant Information Affect App Market Choice?
Wolters, M. K., Li, S., Wang, H., Yang, X., & Guo, Y. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3383078 · Cited by 2
Clearing the Hurdles: How to Design Privacy Nudges for Mobile Application Users
Döbelt, S., Halama, J., Fritsch, S., Nguyen, M.-H., & Bocklisch, F. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50309-3_22 · Cited by 2
A Tag-Based, Logical Access-Control Framework for Personal File Sharing
Mazurek, M. L. (2014) · Figshare · doi:10.1184/r1/6714419 · Cited by 2
Exploring motivations, constraints, and perceptions toward sport consumers' smartphone usage.
Kang, S. (2015) · doi:10.18297/etd/2086 · Cited by 2
A study of privacy-related data collected by Android apps
Khedkar, M., Mondal, A. K., & Bodden, E. (2026) · Automated Software Engineering · doi:10.1007/s10515-025-00589-3 · Cited by 1
von Kalckreuth, N., & Feufel, M. A. (2026) · JMIR Human Factors · doi:10.2196/71124 · Cited by 1
A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI Perspective
Zhang, S., He, E., Tao, S., Yang, Y., Ma, Y., Wang, A., Yi, X., & Li, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790320 · Cited by 2
Balebako, R., Shay, R., & Cranor, L. F. (2018) · Figshare · doi:10.1184/r1/6467951 · Cited by 1
Self-citationA Context-Based Personalization for Mobile Applications’ Network Access
Mowafi, Y., Al-Aqarbeh, T., & Alazrai, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44215-0_34 · Cited by 1
Essential Enabling Technologies
Filip, F. G., Zamfirescu, C.-B., & Ciurea, C. E. (2016) · Automation, collaboration, and e-services · doi:10.1007/978-3-319-47221-8_4 · Cited by 1
Mobile Recommender Apps with Privacy Management for Accessible and Usable Technologies
Hersh, M. A., & Leporini, B. (2017) · Studies in health technology and informatics · doi:10.3233/978-1-61499-798-6-630 · Cited by 1
Wu, H., & Zhang, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68542-7_3 · Cited by 1
RiskMeter: A Tool for Measuring Precise Security Risk Values of Mobile Device Applications
Deypir, M. (2017) · Signal and Data Processing · doi:10.29252/jsdp.14.3.23 · Cited by 1
Assessing User Understanding, Perception and Behaviour with Privacy and Permission Settings
Alshomrani, N., Furnell, S., & He, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35822-7_36 · Cited by 1
Exploring users’ privacy decision making in retail– insights and challenges for HCI research
Henriksen, E. S., Følstad, A., & Boletsis, C. (2025) · Quality and User Experience · doi:10.1007/s41233-025-00074-5 · Cited by 1
Usability und Privacy im Android Ökosystem
Gerber, P., & Volkamer, M. (2015) · Datenschutz und Datensicherheit - DuD · doi:10.1007/s11623-015-0375-y · Cited by 1
Effective Risk Analysis and Risk Detection for Android Apps
Sharad, D., & Patil, B. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016911130 · Cited by 1
Assessment of source data vulnerability to reproduction in Android applications
Shafi, M., Israr, M. B., Khan, M. S., Khattak, M. I., & Syed, T. A. (2017) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn.2017.8319369 · Cited by 1
Safety Priming and Subsequent App Selection Behaviors
Chong, I., Ge, H., Proctor, R. W., & Li, N. (2018) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931218621069 · Cited by 1
Understanding Privacy-Focused Technology Use Among Generation Y
Agozie, D. Q., Nat, M. C., & Edu, S. A. (2020) · Advances in information quality and management · doi:10.4018/978-1-7998-2610-1.ch004 · Cited by 1
Untact Management of Patients with Chronic Kidney Diseases
Yoo, J., Su, A., & Yoo, S. (2021) · Korean Journal of Medicine · doi:10.3904/kjm.2021.96.4.253 · Cited by 1
Willingness to grant access to personal information among augmented reality mobile app users
Taub, G., Elmalech, A., & Aharony, N. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01700-1 · Cited by 1
Cai, Z., & Xiong, A. (2022) · Workshop on Usable Security (USEC) · doi:10.14722/usec.2022.23036 · Cited by 1
Privacy impact self-assessment app
Toresson, L., Olars, S., & Shaker, M. (2020) · Digitala vetenskapliga arkivet (Diva) (Karlstad University) · Cited by 1
Beyond the Privacy Calculus: Dynamics Behind Online Self-DisclosurePreprint
Wagner, A. (2021) · RePEc: Research Papers in Economics · doi:10.26083/tuprints-00019049 · Cited by 1
Rony, R. J., Saliheen, N., & Ahmed, N. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.4014453 · Cited by 1
Mitigating the Risks of Smartphone Data Sharing: Identifying Opportunities and Evaluating Notice
Balebako, R. (2014) · Figshare · doi:10.1184/r1/6720635 · Cited by 1
Functionalities as Superior Predictor of Applications Privacy Threats
Santo, A. D., Quiquerez, B., & Gaspoz, C. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-31232-3_25
Johnson, M., Riva, O., Jung, J., & Wagner, D. (2013)
Bhargava, P., Gramsky, N., & Agrawala, A. K. (2016) · EAI Endorsed Transactions on Context-aware Systems and Applications · doi:10.4108/eai.12-9-2016.151676
Prachi, & Jain, A. (2017) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-5225-2154-9.ch004
Using Machine Learning to improve Internet Privacy
Zimmeck, S. (2017) · Columbia Academic Commons (Columbia University) · doi:10.7916/d8862n5f
Loon, D. P. C. W., & Kumar, S. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2255-3.ch539
Contextual Permission Models for Better Privacy Protection
Wijesekera, P. (2018) · doi:10.14288/1.0368657
Towards Measuring Apps' Privacy-Friendliness
Momen, N. (2018) · Diva portal (Dalarna University Library)
Utility-Aware and Privacy-Preserving Mobile Query Services
Yigitoglu, E., Gürsoy, M. E., & Liu, L. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3170007
Using Authorization Logic to Capture User Policies in Mobile Ecosystems
Hallett, J., & Aspinall, D. (2015) · Bristol Research (University of Bristol)
Program Analysis Based Approaches to Ensure Security and Safety of Emerging Software Platforms
Jia, Y. (2018) · Deep Blue (University of Michigan)
Helping People to Control Their Everyday Data for Care: A Scenario-Based Study
Hung, P.-Y., & Ackerman, M. S. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-99194-4_18
Loon, D. P. C. W., & Kumar, S. (2018) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-7598-6.ch074
Bibliographie sélective classée par thématiques
(2023) · ENS Éditions eBooks · doi:10.4000/books.enseditions.44998
Security of Human Machine Systems
Flammini, F., Bellini, E., de Biase, M. S., & Marrone, S. (2023) · doi:10.1002/9781119863663.ch34
Users’ Sophisticated Information Search Behaviour
Alhejaili, A., & Blustein, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35921-7_1
Cofone, I. (2023) · doi:10.1017/9781108995825.010
Poster: Longitudinal Measurement of the Adoption Dynamics in Apple's Privacy Label Ecosystem
Balash, D. G., Ali, M. M., Kodwani, M., Wu, X., Kanich, C., & Aviv, A. J. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3624383
"Shadow Data Flows" in Apps as a Systematic Flaw – Good Design Patterns and Privdevops to the RescuePreprint
Tank, F. T., Harborth, D., & Doni, I. V. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4766979
Metadata
- Authors
- Kelley, P. G., Cranor, L. F., & Sadeh, N.
- Affiliations
- University of New Mexico (US)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2013-04-27
- Pages
- 3393-3402
- OpenAlex
- W2007784529
- Cited by
- 314
- References
- 17
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 13 · 2023: 27 · 2022: 34 · 2021: 27 · 2020: 23 · 2019: 15 · 2018: 33 · 2017: 33 · 2016: 32 · 2015: 28 · 2014: 22 · 2013: 5
- FWCI
- 90.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation
- Carnegie Mellon University
- Army Research Office
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- application permissions, privacy, user decision-making, personal information disclosure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
