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As a key supplement to privacy policies that are known to be lengthy and difficult to read, Apple has launched the app privacy labels, which purportedly help users more easily understand an app's privacy practices.
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Ray Smith · 2007
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A" nutrition label" for privacy
Patrick Gage Kelley, Joanna Bresee, Lorrie Faith Cranor, and Robert W Reeder · 2009
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Exploring end user preferences for location obfuscation, location-based services, and the value of location
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Standardizing privacy notices: an online study of the nutrition label approach
Patrick Gage Kelley, Lucian Cesca, Joanna Bresee, and Lorrie Faith Cranor · 2010
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Understanding bag-of-words model: a statistical framework
Yin Zhang, Rong Jin, and Zhi-Hua Zhou · 2010
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Pios: Detecting privacy leaks in ios applications
Manuel Egele, Christopher Kruegel, Engin Kirda, and Giovanni Vigna · 2011
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Scikit-learn: Machine learning in python
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Unsafe exposure analysis of mobile in-app advertisements
Michael C Grace, Wu Zhou, Xuxian Jiang, and Ahmad-Reza Sadeghi · 2012
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Formal analysis of privacy requirements specifications for multi-tier applications
Travis D Breaux and Ashwini Rao · 2013
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Comparing mobile privacy protection through cross-platform applications
Jin Han, Qiang Yan, Debin Gao, Jianying Zhou, and Robert H Deng · 2013
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Privacy as part of the app decision-making process
Patrick Gage Kelley, Lorrie Faith Cranor, and Norman Sadeh · 2013
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Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
Steven Arzt, Siegfried Rasthofer, Christian Fritz, Eric Bodden, Alexandre Bartel, Jacques Klein, Yves Le Traon, Damien Octeau, and Patrick McDaniel · 2014
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iris: Vetting private api abuse in ios applications
Zhui Deng, Brendan Saltaformaggio, Xiangyu Zhang, and Dongyan Xu · 2015
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Information flow analysis of android applications in droidsafe
Michael I Gordon, Deokhwan Kim, Jeff H Perkins, Limei Gilham, Nguyen Nguyen, and Martin C Rinard · 2015
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Uipicker: User-input privacy identification in mobile applications
Yuhong Nan, Min Yang, Zhemin Yang, Shunfan Zhou, Guofei Gu, and XiaoFeng Wang · 2015
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Do not track initiatives: regaining the lost user control
Irene Kamara and Eleni Kosta · 2016
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PolicyLint: Investigating internal privacy policy contradictions on google play
Benjamin Andow, Samin Yaseer Mahmud, Wenyu Wang, Justin Whitaker, William Enck, Bradley Reaves, Kapil Singh, and Tao Xie · 2019
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Demystifying hidden privacy settings in mobile apps
Yi Chen, Mingming Zha, Nan Zhang, Dandan Xu, Qianqian Zhao, Xuan Feng, Kan Yuan, Fnu Suya, Yuan Tian, and Kai Chen · 2019
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Exploring how privacy and security factor into iot device purchase behavior
Pardis Emami-Naeini, Henry Dixon, Yuvraj Agarwal, and Lorrie Faith Cranor · 2019
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Maps: Scaling privacy compliance analysis to a million apps
Sebastian Zimmeck, Peter Story, Daniel Smullen, Abhilasha Ravichander, Ziqi Wang, Joel R Reidenberg, N Cameron Russell, and Norman Sadeh · 2019
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https://developers.facebook.com/blog/post/2020/10/22/preparing-for-apple-app-store-data-disclosure-requirements/
Facebook SDK guidance · 2020
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Rocky Slavin, Xiaoyin Wang, Mitra Bokaei Hosseini, James Hester, Ram Krishnan, Jaspreet Bhatia, Travis D Breaux, and Jianwei Niu · 2016
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Can we trust the privacy policies of android apps?
Le Yu, Xiapu Luo, Xule Liu, and Tao Zhang · 2016
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Automated analysis of privacy requirements for mobile apps
Sebastian Zimmeck, Ziqi Wang, Lieyong Zou, Roger Iyengar, Bin Liu, Florian Schaub, Shomir Wilson, Norman Sadeh, Steven Bellovin, and Joel Reidenberg · 2016
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Finding clues for your secrets: Semantics-driven, learning-based privacy discovery in mobile apps
Yuhong Nan, Zhemin Yang, Xiaofeng Wang, Yuan Zhang, Donglai Zhu, and Min Yang · 2018
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Apps, trackers, privacy, and regulators: A global study of the mobile tracking ecosystem
Abbas Razaghpanah, Rishab Nithyanand, Narseo Vallina-Rodriguez, Srikanth Sundaresan, Mark Allman, Christian Kreibich, Phillipa Gill, et al · 2018
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Guileak: Tracing privacy policy claims on user input data for android applications
Xiaoyin Wang, Xue Qin, Mitra Bokaei Hosseini, Rocky Slavin, Travis D Breaux, and Jianwei Niu · 2018
Cited alongside, same era.
Ask the experts: What should be on an iot privacy and security label?
Pardis Emami-Naeini, Yuvraj Agarwal, Lorrie Faith Cranor, and Hanan Hibshi · 2020
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https://energycommerce.house.gov/sites/democrats.energycommerce.house.gov/files/documents/Apple%20Letter%20re%20App%20Privacy%20Label%202-2021.pdf
Concerns of Privacy Label Accuracy · 2021
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https://developer.apple.com/app-store/app-privacy-details/
App privacy details on the App Store, 2021 · 2021
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Consistency analysis of data-usage purposes in mobile apps
Duc Bui, Yuan Yao, Kang G Shin, Jong-Min Choi, and Junbum Shin · 2021
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Understanding malicious cross-library data harvesting on android
Jice Wang, Yue Xiao, Xueqiang Wang, Yuhong Nan, Luyi Xing, Xiaojing Liao, JinWei Dong, Nicolas Serrano, Haoran Lu, XiaoFeng Wang, et al · 2021
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Understanding challenges for developers to create accurate privacy nutrition labels
Tianshi Li, Kayla Reiman, Yuvraj Agarwal, Lorrie Faith Cranor, and Jason I Hong · 2022
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Understanding ios privacy nutrition labels: An exploratory large-scale analysis of app store data
Yucheng Li, Deyuan Chen, Tianshi Li, Yuvraj Agarwal, Lorrie Faith Cranor, and Jason I Hong · 2022
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