An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps
暂无分享,去创建一个
Yang Liu | Ming Fan | Ting Liu | Hao Zhou | Jun Liu | Sen Chen | Le Yu | Xiapu Luo | Shuyue Li | Xiapu Luo | Yang Liu | Le Yu | Ting Liu | Sen Chen | Ming Fan | Jun Liu | Hao Zhou | Shuyue Li
[1] Guido Governatori,et al. Modelling Legal Knowledge for GDPR Compliance Checking , 2018, JURIX.
[2] Tao Xie,et al. Automated extraction of security policies from natural-language software documents , 2012, SIGSOFT FSE.
[3] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[4] Qinghua Zheng,et al. Graph Embedding Based Familial Analysis of Android Malware using Unsupervised Learning , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).
[5] Bin Liu,et al. Automated Analysis of Privacy Requirements for Mobile Apps , 2016, NDSS.
[6] Peng Wang,et al. AsDroid: detecting stealthy behaviors in Android applications by user interface and program behavior contradiction , 2014, ICSE.
[7] Liliana Pasquale,et al. The Grace Period Has Ended: An Approach to Operationalize GDPR Requirements , 2018, 2018 IEEE 26th International Requirements Engineering Conference (RE).
[8] Yan Wang,et al. Static Window Transition Graphs for Android (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[9] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[10] Kang G. Shin,et al. Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning , 2018, USENIX Security Symposium.
[11] William K. Robertson,et al. Hidden GEMs: Automated Discovery of Access Control Vulnerabilities in Graphical User Interfaces , 2014, 2014 IEEE Symposium on Security and Privacy.
[12] David Brumley,et al. An empirical study of cryptographic misuse in android applications , 2013, CCS.
[13] Iulian Neamtiu,et al. Targeted and depth-first exploration for systematic testing of android apps , 2013, OOPSLA.
[14] Xue Qin,et al. GUILeak: Tracing Privacy Policy Claims on User Input Data for Android Applications , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[15] Bernd Freisleben,et al. Why eve and mallory love android: an analysis of android SSL (in)security , 2012, CCS.
[16] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[17] Mehrdad Sabetzadeh,et al. Using Models to Enable Compliance Checking Against the GDPR: An Experience Report , 2019, 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS).
[18] Xiangyu Zhang,et al. SUPOR: Precise and Scalable Sensitive User Input Detection for Android Apps , 2015, USENIX Security Symposium.
[19] Travis D. Breaux,et al. An Evaluation of Constituency-Based Hyponymy Extraction from Privacy Policies , 2017, 2017 IEEE 25th International Requirements Engineering Conference (RE).
[20] Qinghua Zheng,et al. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis , 2018, IEEE Transactions on Information Forensics and Security.
[21] Travis D. Breaux,et al. Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition , 2016, ACM Trans. Softw. Eng. Methodol..
[22] Chao Yang,et al. Who is peeping at your passwords at Starbucks? — To catch an evil twin access point , 2010, 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN).
[23] Collin Jackson,et al. Forcehttps: protecting high-security web sites from network attacks , 2008, WWW.
[24] Laurie A. Williams,et al. How Good Is a Security Policy against Real Breaches? A HIPAA Case Study , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE).
[25] Dengfeng Li,et al. UiRef: analysis of sensitive user inputs in Android applications , 2017, WISEC.
[26] Xiaofeng Wang,et al. UIPicker: User-Input Privacy Identification in Mobile Applications , 2015, USENIX Security Symposium.
[27] E Moss,et al. The National Health Data Dictionary , 1994, Health information management : journal of the Health Information Management Association of Australia.
[28] Jun Liu,et al. CTDroid: Leveraging a Corpus of Technical Blogs for Android Malware Analysis , 2020, IEEE Transactions on Reliability.
[29] Laurie Hendren,et al. Soot: a Java bytecode optimization framework , 2010, CASCON.
[30] Murat Kantarcioglu,et al. CryptoGuard: High Precision Detection of Cryptographic Vulnerabilities in Massive-sized Java Projects , 2018, CCS.
[31] Rong Jin,et al. Understanding bag-of-words model: a statistical framework , 2010, Int. J. Mach. Learn. Cybern..
[32] Eric Bodden,et al. A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks , 2014, NDSS.
[33] Jacques Klein,et al. IccTA: Detecting Inter-Component Privacy Leaks in Android Apps , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[34] Christopher Krügel,et al. EdgeMiner: Automatically Detecting Implicit Control Flow Transitions through the Android Framework , 2015, NDSS.
[35] Clare-Marie Karat,et al. An empirical study of natural language parsing of privacy policy rules using the SPARCLE policy workbench , 2006, SOUPS '06.
[36] Jacques Klein,et al. FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps , 2014, PLDI.
[37] Aristide Fattori,et al. CopperDroid: Automatic Reconstruction of Android Malware Behaviors , 2015, NDSS.
[38] Tao Zhang,et al. Can We Trust the Privacy Policies of Android Apps? , 2016, 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
[39] J. Murphy. The General Data Protection Regulation (GDPR) , 2018, Irish medical journal.
[40] Qinghua Zheng,et al. Frequent Subgraph Based Familial Classification of Android Malware , 2016, 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE).
[41] Ming Fan,et al. DAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis , 2017, IEEE Transactions on Information Forensics and Security.
[42] Ram Krishnan,et al. Toward a Framework for Detecting Privacy Policy Violations in Android Application Code , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[43] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[44] Tao Xie,et al. PolicyLint: Investigating Internal Privacy Policy Contradictions on Google Play , 2019, USENIX Security Symposium.
[45] Michael Backes,et al. A Stitch in Time: Supporting Android Developers in WritingSecure Code , 2017, CCS.
[46] Yu Le,et al. VulHunter: Toward Discovering Vulnerabilities in Android Applications , 2015, IEEE Micro.
[47] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[48] Yajin Zhou,et al. Malton: Towards On-Device Non-Invasive Mobile Malware Analysis for ART , 2017, USENIX Security Symposium.
[49] Lionel Briand,et al. An AI-assisted Approach for Checking the Completeness of Privacy Policies Against GDPR , 2020, 2020 IEEE 28th International Requirements Engineering Conference (RE).
[50] Raimundas Matulevicius,et al. Conceptual Representation of the GDPR: Model and Application Directions , 2018, BIR.
[51] Jerry den Hartog,et al. What Websites Know About You , 2012, DPM/SETOP.
[52] Travis D. Breaux,et al. A Theory of Vagueness and Privacy Risk Perception , 2016, 2016 IEEE 24th International Requirements Engineering Conference (RE).
[53] Nick Feamster,et al. Cleartext Data Transmissions in Consumer IoT Medical Devices , 2017, IoT S&P@CCS.