PTPDroid: Detecting Violated User Privacy Disclosures to Third-Parties of Android Apps
Android apps frequently access personal information to provide customized services. Since such information is sensitive in general, regulators require Android app vendors to publish privacy policies that describe what information is collected and why it is collected. Existing work mainly focuses on the types of the collected data but seldom considers the entities that collect user privacy, which could falsely classify problematic declarations about user privacy collected by third-parties into clear disclosures. To address this problem, we propose \textsf{PTPDroid}, a flow-to-policy consistency checking approach and an automated tool, to comprehensively uncover from the privacy policy the violated disclosures to third-parties. Our experiments on real-world apps demonstrate the effectiveness and superiority of \textsf{PTPDroid}, and our empirical study on 1,000 popular real-world apps reveals that violated user privacy disclosures to third-parties are prevalent in practice.
Wed 17 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Software security and privacyTechnical Track / Journal-First Papers at Meeting Room 103 Chair(s): Wei Yang University of Texas at Dallas | ||
13:45 15mTalk | BFTDetector: Automatic Detection of Business Flow Tampering for Digital Content Service Technical Track I Luk Kim Purdue University, Weihang Wang University of Southern California, Yonghwi Kwon University of Virginia, Xiangyu Zhang Purdue University | ||
14:00 15mTalk | FedSlice: Protecting Federated Learning Models from Malicious Participants with Model Slicing Technical Track Ziqi Zhang Peking University, Yuanchun Li Institute for AI Industry Research (AIR), Tsinghua University, Bingyan Liu Peking University, Yifeng Cai Peking University, Ding Li Peking University, Yao Guo Peking University, Xiangqun Chen Peking University | ||
14:15 15mTalk | PTPDroid: Detecting Violated User Privacy Disclosures to Third-Parties of Android Apps Technical Track Zeya Tan Nanjing University of Science and Technology, Wei Song Nanjing University of Science and Technology Pre-print | ||
14:30 15mTalk | AdHere: Automated Detection and Repair of Intrusive Ads Technical Track Yutian Yan University of Southern California, Yunhui Zheng , Xinyue Liu University at Buffalo, SUNY, Nenad Medvidović University of Southern California, Weihang Wang University of Southern California | ||
14:45 15mTalk | Bad Snakes: Understanding and Improving Python Package Index Malware Scanning Technical Track | ||
15:00 7mTalk | DAISY: Dynamic-Analysis-Induced Source Discovery for Sensitive Data Journal-First Papers Xueling Zhang Rochester Institute of Technology, John Heaps University of Texas at San Antonio, Rocky Slavin The University of Texas at San Antonio, Jianwei Niu University of Texas at San Antonio, Travis Breaux Carnegie Mellon University, Xiaoyin Wang University of Texas at San Antonio | ||
15:07 7mTalk | Assessing the opportunity of combining state-of-the-art Android malware detectors Journal-First Papers Nadia Daoudi SnT, University of Luxembourg, Kevin Allix CentraleSupelec Rennes, Tegawendé F. Bissyandé SnT, University of Luxembourg, Jacques Klein University of Luxembourg | ||