Algorithms that remember: model inversion attacks and data protection law
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[1] Somesh Jha,et al. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing , 2014, USENIX Security Symposium.
[2] Luciano Floridi,et al. Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation , 2017 .
[3] Krishna P. Gummadi,et al. Blind Justice: Fairness with Encrypted Sensitive Attributes , 2018, ICML.
[4] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[5] Sofya Raskhodnikova,et al. What Can We Learn Privately? , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[6] Michel van Eeten,et al. Collectively exercising the right of access: individual effort, societal effect , 2017, Internet Policy Rev..
[7] Junfeng Yang,et al. Towards Making Systems Forget with Machine Unlearning , 2015, 2015 IEEE Symposium on Security and Privacy.
[8] Rathindra Sarathy,et al. Does Differential Privacy Protect Terry Gross' Privacy? , 2010, Privacy in Statistical Databases.
[9] Jun Zhao,et al. Third Party Tracking in the Mobile Ecosystem , 2018, WebSci.
[10] Martín Abadi,et al. On the Protection of Private Information in Machine Learning Systems: Two Recent Approches , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).
[11] Beth-Anne Schuelke-Leech,et al. Smart Technologies and the End(s) of Law , 2017 .
[12] Jon Crowcroft,et al. Unclouded Vision , 2011, ICDCN.
[13] Stephen J. Cox,et al. Speaker-independent machine lip-reading with speaker-dependent viseme classifiers , 2015, AVSP.
[14] Athanasios V. Vasilakos,et al. Cloud Computing , 2014, ACM Comput. Surv..
[15] Emiliano De Cristofaro,et al. Knock Knock, Who's There? Membership Inference on Aggregate Location Data , 2017, NDSS.
[16] M. I. V. Eale,et al. SLAVE TO THE ALGORITHM ? WHY A ‘ RIGHT TO AN EXPLANATION ’ IS PROBABLY NOT THE REMEDY YOU ARE LOOKING FOR , 2017 .
[17] Somesh Jha,et al. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures , 2015, CCS.
[18] David Banisar,et al. The Right to Information and Privacy: Balancing Rights and Managing Conflicts , 2011 .
[19] Kieron O'Hara,et al. Functional anonymisation: Personal data and the data environment , 2018, Comput. Law Secur. Rev..
[20] Anand D. Sarwate,et al. Differentially Private Empirical Risk Minimization , 2009, J. Mach. Learn. Res..
[21] Brent Mittelstadt,et al. From Individual to Group Privacy in Big Data Analytics , 2017 .
[22] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.
[23] Nadezhda Purtova. The law of everything. Broad concept of personal data and future of EU data protection law , 2018 .
[24] Mireille Hildebrandt,et al. Profiling and the rule of law , 2008 .
[25] Y. Hoffman. Knock! Knock! Who's there? , 1995, Michigan health & hospitals.
[26] Charles Elkan,et al. Differential Privacy and Machine Learning: a Survey and Review , 2014, ArXiv.
[27] Úlfar Erlingsson,et al. The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets , 2018, ArXiv.
[28] Anton Vedder,et al. KDD: The challenge to individualism , 1999, Ethics and Information Technology.
[29] Michael Veale,et al. When data protection by design and data subject rights clash , 2018 .
[30] Vitaly Shmatikov,et al. 2011 IEEE Symposium on Security and Privacy “You Might Also Like:” Privacy Risks of Collaborative Filtering , 2022 .
[31] Fan Zhang,et al. Stealing Machine Learning Models via Prediction APIs , 2016, USENIX Security Symposium.
[32] Somesh Jha,et al. The Unintended Consequences of Overfitting: Training Data Inference Attacks , 2017, ArXiv.
[33] Paul Ohm. Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization , 2009 .
[34] Giovanni Felici,et al. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers , 2013, Int. J. Secur. Networks.
[35] Hamed Haddadi,et al. Enabling the new economic actor: data protection, the digital economy, and the Databox , 2016, Personal and Ubiquitous Computing.
[36] Alexander Russell,et al. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data , 2016, 2016 IEEE Wireless Health (WH).
[37] Paul De Hert,et al. The unaccountable state of surveillance: Exercising access rights in Europe , 2017 .
[38] Jun Zhao,et al. Better the Devil You Know: Exposing the Data Sharing Practices of Smartphone Apps , 2017, CHI.
[39] Vitaly Shmatikov,et al. Membership Inference Attacks Against Machine Learning Models , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[40] Cynthia Dwork,et al. Differential Privacy: A Survey of Results , 2008, TAMC.
[41] R. Sarathy,et al. Fool's Gold: an Illustrated Critique of Differential Privacy , 2013 .
[42] Zhi-Hua Zhou,et al. Learnware: on the future of machine learning , 2016, Frontiers of Computer Science.
[43] Julia Powles,et al. "Meaningful Information" and the Right to Explanation , 2017, FAT.
[44] Paul De Hert,et al. Exercising Access Rights in Belgium , 2017 .
[45] Vitaly Shmatikov,et al. Machine Learning Models that Remember Too Much , 2017, CCS.
[46] Ilya Mironov,et al. On significance of the least significant bits for differential privacy , 2012, CCS.
[47] Thomas Steinke,et al. Differential Privacy: A Primer for a Non-Technical Audience , 2018 .
[48] Jatinder Singh,et al. Decision Provenance: Capturing data flow for accountable systems , 2018, ArXiv.