cs763/website/docs/resources/readings.md

Assorted Papers

Differential Privacy

• Frank McSherry and Kunal Talwar. Mechanism Design via Differential Privacy. FOCS 2007.
• Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy Rothblum. Differential Privacy under Continual Observation. STOC 2010.
• T.-H. Hubert Chan, Elaine Shi, and Dawn Song. Private and Continual Release of Statistics. ICALP 2010.
• Ilya Mironov. On Significance of the Least Significant Bits For Differential Privacy. CCS 2012.
• Moritz Hardt, Katrina Ligett, and Frank McSherry. A Simple and Practical Algorithm for Differentially Private Data Release. NIPS 2012.
• Daniel Kifer and Ashwin Machanavajjhala. A Rigorous and Customizable Framework for Privacy. PODS 2012.
• Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. CCS 2014.
• Cynthia Dwork, Moni Naor, Omer Reingold, and Guy N. Rothblum. Pure Differential Privacy for Rectangle Queries via Private Partitions. ASIACRYPT 2015.
• Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep Learning with Differential Privacy. CCS 2016.
• Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, and Li Zhang. On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches. CSF 2016.
• Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. ICLR 2017.
• Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. Scalable Private Learning with PATE. ICLR 2018.
• Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner. Local Differential Privacy for Evolving Data. NeurIPS 2018.
• Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev. Distributed Differential Privacy via Shuffling. EUROCRYPT 2019.
• Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta. Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity. SODA 2019.
• Jingcheng Liu and Kunal Talwar. Private Selection from Private Candidates. STOC 2019.

Adversarial ML

• Battista Biggio, Blaine Nelson, and Pavel Laskov. Poisoning Attacks against Support Vector Machines. ICML 2012.
• Battista Biggio, Ignazio Pillai, Samuel Rota Bulò, Davide Ariu, Marcello Pelillo, and Fabio Roli. Is Data Clustering in Adversarial Settings Secure?. AISec 2013.
• Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing Properties of Neural Networks. ICLR 2014.
• Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and Harnessing Adversarial Examples. ICLR 2015.
• Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. CCS 2015.
• Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples. arXiv 2016.
• Nicholas Carlini and David Wagner. Towards Evaluating the Robustness of Neural Networks. S&P 2017.
• Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership Inference Attacks against Machine Learning Models. S&P 2017.
• Nicholas Carlini and David Wagner. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. AISec 2017.
• Jacob Steinhardt, Pang Wei Koh, and Percy Liang. Certified Defenses for Data Poisoning Attacks. NIPS 2017.
• Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust Physical-World Attacks on Deep Learning Models. CVPR 2018.
• Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR 2018.
• Aditi Raghunathan, Jacob Steinhardt, and Percy Liang. Certified Defenses against Adversarial Examples. ICLR 2018.
• Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble Adversarial Training: Attacks and Defenses. ICLR 2018.
• Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. Poison Frogs! Targeted Clean-Label PoisoningAttacks on Neural Networks. NeurIPS 2019.
• Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. USENIX 2019.
• Vitaly Feldman. Does Learning Require Memorization? A Short Tale about a Long Tail. STOC 2020.

Applied Cryptography

• Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish. Verifying Computations with State. SOSP 2013.
• Bryan Parno, Jon Howell, Craig Gentry, and Mariana Raykova. Pinocchio: Nearly Practical Verifiable Computation. S&P 2013.
• Aseem Rastogi, Matthew A. Hammer and Michael Hicks. Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations. S&P 2014.
• Shai Halevi and Victor Shoup. Algorithms in HElib. CRYPTO 2014.
• Shai Halevi and Victor Shoup. Bootstrapping for HElib. EUROCRYPT 2015.
• Léo Ducas and Daniele Micciancio. FHEW: Bootstrapping Homomorphic Encryption in Less than a Second. EUROCRYPT 2015.
• Peter Kairouz, Sewoong Oh, and Pramod Viswanath. Secure Multi-party Differential Privacy. NIPS 2015.
• Arjun Narayan, Ariel Feldman, Antonis Papadimitriou, and Andreas Haeberlen. Verifiable Differential Privacy. EUROSYS 2015.
• Henry Corrigan-Gibbs and Dan Boneh. Prio: Private, Robust, and Scalable Computation of Aggregate Statistics. NSDI 2017.
• Zahra Ghodsi, Tianyu Gu, Siddharth Garg. SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud. NIPS 2017.
• Valerie Chen, Valerio Pastro, Mariana Raykova. Secure Computation for Machine Learning With SPDZ. NeurIPS 2018.
• Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph. Stoecklin, Heqing Huang, and Ian Molloy. Protecting Intellectual Property of Deep Neural Networks with Watermarking. AsiaCCS 2018.
• Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet. Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring. USENIX 2018.
• Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica. Helen: Maliciously Secure Coopetitive Learning for Linear Models. S&P 2019.
• Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar. DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models. ASPLOS 2019.
• Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, and Todd Mytkowicz. CHET: an optimizing compiler for fully-homomorphic neural-network inferencing. PLDI 2019.

Algorithmic Fairness

• Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. Fairness through Awarness. ITCS 2012.
• Moritz Hardt, Eric Price, and Nathan Srebro. Equality of Opportunity in Supervised Learning. NIPS 2016.
• Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NIPS 2016.
• Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. EMNLP 2017.
• Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS 2017.
• Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. Avoiding Discrimination through Causal Reasoning. NIPS 2017.
• Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva. Counterfactual Fairness. NIPS 2017.
• Razieh Nabi and Ilya Shpitser. Fair Inference on Outcomes. AAAI 2018.
• Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum. Multicalibration: Calibration for the (Computationally-Identifiable) Masses. ICML 2018.
• Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018.
• Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. A Reductions Approach to Fair Classification. ICML 2019.
• Ben Hutchinson and Margaret Mitchell. 50 Years of Test (Un)fairness: Lessons for Machine Learning. FAT* 2019.

PL and Verification

• Martín Abadi and Andrew D. Gordon. A Calculus for Cryptographic Protocols: The Spi Calculus. Information and Computation, 1999.
• Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum. Church: a language for generative models. UAI 2008.
• Frank McSherry. Privacy Integrated Queries. SIGMOD 2009.
• Marta Kwiatkowska, Gethin Norman, and David Parker. Advances and Challenges of Probabilistic Model Checking. Allerton 2010.
• Jason Reed and Benjamin C. Pierce. Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy. ICFP 2010.
• Daniel B. Griffin, Amit Levy, Deian Stefan, David Terei, David Mazières, John C. Mitchell, and Alejandro Russo. Hails: Protecting Data Privacy in Untrusted Web Applications. OSDI 2012.
• Danfeng Zhang, Aslan Askarov, and Andrew C. Myers. Language-Based Control and Mitigation of Timing Channels. PLDI 2012.
• Andrew Miller, Michael Hicks, Jonathan Katz, and Elaine Shi. Authenticated Data Structures, Generically. POPL 2014.
• Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani. Probabilistic Programming. ICSE 2014.
• Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, and Pierre-Yves Strub. Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. POPL 2015.
• Samee Zahur and David Evans. Obliv-C: A Language for Extensible Data-Oblivious Computation. IACR 2015.
• Chang Liu, Xiao Shaun Wang, Kartik Nayak, Yan Huang, and Elaine Shi. ObliVM: A Programming Framework for Secure Computation. S&P 2015.
• Gilles Barthe, Marco Gaboardi, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub. A Program Logic for Union Bounds. ICALP 2016.
• Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin, and Radu State. Interpreting Finite Automata for Sequential Data. NIPS 2016.
• Joost-Pieter Katoen. The Probabilistic Model Checking Landscape. LICS 2016.
• Andrew Ferraiuolo, Rui Xu, Danfeng Zhang, Andrew C. Myers, and G. Edward Suh. Verification of a Practical Hardware Security Architecture Through Static Information Flow Analysis. ASPLOS 2017.
• Frits Vaandrager. Model Learning. CACM 2017.
• Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation. S&P 2018.
• Matthew Mirman, Timon Gehr, and Martin Vechev. Differentiable Abstract Interpretation for Provably Robust Neural Networks. ICML 2018.
• Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. Automatic differentiation in machine learning: a survey. JMLR 2018.
• Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin T. Vechev. An Abstract Domain for Certifying Neural Networks. POPL 2019.
• Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, and Martin Vechev. DL2: Training and Querying Neural Networks with Logic. ICML 2019.
• Abhinav Verma, Hoang M. Le, Yisong Yue, and Swarat Chaudhuri. Imitation-Projected Programmatic Reinforcement Learning. NeurIPS 2019.
• Kenneth L. McMillan Bayesian Interpolants as Explanations for Neural Inferences. arXiv.

Supplemental Material

• Cynthia Dwork and Aaron Roth. Algorithmic Foundations of Data Privacy.
• Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities.
• Gilles Barthe, Marco Gaboardi, Justin Hsu, and Benjamin C. Pierce. Programming Language Techniques for Differential Privacy.
• Michael Walfish and Andrew J. Blumberg. Verifying Computations without Reexecuting Them.
• Véronique Cortier, Steve Kremer, and Bogdan Warinschi. A Survey of Symbolic Methods in Computational Analysis of Cryptographic Systems.
• Dan Boneh and Victor Shoup. A Graduate Course in Applied Cryptography.
• David Hand. Statistics and the Theory of Measurement.
• Judea Pearl. Causal inference in statistics: An overview.
• Judea Pearl. Understanding Simpson’s Paradox.
• Yehuda Lindell and Benny Pinkas. Secure Multiparty Computation for Privacy-Preserving Data Mining.