# Assorted Papers ### Differential Privacy - Frank McSherry and Kunal Talwar. [*Mechanism Design via Differential Privacy*](http://kunaltalwar.org/papers/expmech.pdf). FOCS 2007. - Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy Rothblum. [*Differential Privacy under Continual Observation*](http://www.wisdom.weizmann.ac.il/~naor/PAPERS/continual_observation.pdf). STOC 2010. - T.-H. Hubert Chan, Elaine Shi, and Dawn Song. [*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf). ICALP 2010. - Ilya Mironov. [*On Significance of the Least Significant Bits For Differential Privacy*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.366.5957&rep=rep1&type=pdf). CCS 2012. - Moritz Hardt, Katrina Ligett, and Frank McSherry. [*A Simple and Practical Algorithm for Differentially Private Data Release*](https://papers.nips.cc/paper/4548-a-simple-and-practical-algorithm-for-differentially-private-data-release.pdf). NIPS 2012. - Daniel Kifer and Ashwin Machanavajjhala. [*A Rigorous and Customizable Framework for Privacy*](http://www.cse.psu.edu/~duk17/papers/pufferfish_preprint.pdf). PODS 2012. - Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf). CCS 2014. - Cynthia Dwork, Moni Naor, Omer Reingold, and Guy N. Rothblum. [*Pure Differential Privacy for Rectangle Queries via Private Partitions*](https://guyrothblum.files.wordpress.com/2017/06/dnrr15.pdf). ASIACRYPT 2015. - Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. [*Deep Learning with Differential Privacy*](https://arxiv.org/pdf/1607.00133). 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*](https://arxiv.org/pdf/1708.08022). CSF 2016. - Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar. [*Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data*](https://arxiv.org/pdf/1610.05755). ICLR 2017. - Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. [*Scalable Private Learning with PATE*](https://arxiv.org/pdf/1802.08908). ICLR 2018. - Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner. [*Local Differential Privacy for Evolving Data*](https://arxiv.org/abs/1802.07128). NeurIPS 2018. - Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev. [*Distributed Differential Privacy via Shuffling*](https://arxiv.org/pdf/1808.01394). 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*](https://arxiv.org/pdf/1811.12469). SODA 2019. - Jingcheng Liu and Kunal Talwar. [*Private Selection from Private Candidates*](https://arxiv.org/pdf/1811.07971). STOC 2019. ### Adversarial ML - Battista Biggio, Blaine Nelson, and Pavel Laskov. [*Poisoning Attacks against Support Vector Machines*](https://arxiv.org/pdf/1206.6389). ICML 2012. - Battista Biggio, Ignazio Pillai, Samuel Rota Bulò, Davide Ariu, Marcello Pelillo, and Fabio Roli. [*Is Data Clustering in Adversarial Settings Secure?*](https://arxiv.org/abs/1811.09982). AISec 2013. - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf). ICLR 2014. - Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572). ICLR 2015. - Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. [*Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures*](https://www.cs.cmu.edu/~mfredrik/papers/fjr2015ccs.pdf). CCS 2015. - Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. [*Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples*](https://arxiv.org/abs/1605.07277). arXiv 2016. - Nicholas Carlini and David Wagner. [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf). S&P 2017. - Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. [*Membership Inference Attacks against Machine Learning Models*](https://arxiv.org/pdf/1610.05820). S&P 2017. - Nicholas Carlini and David Wagner. [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf). AISec 2017. - Jacob Steinhardt, Pang Wei Koh, and Percy Liang. [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf). 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*](https://arxiv.org/pdf/1707.08945.pdf). CVPR 2018. - Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf). ICLR 2018. - Aditi Raghunathan, Jacob Steinhardt, and Percy Liang. [*Certified Defenses against Adversarial Examples*](https://arxiv.org/pdf/1801.09344). ICLR 2018. - Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. [*Ensemble Adversarial Training: Attacks and Defenses*](https://arxiv.org/pdf/1705.07204). 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*](https://arxiv.org/pdf/1804.00792). NeurIPS 2019. - Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. [*The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks*](https://arxiv.org/pdf/1802.08232). USENIX 2019. - Vitaly Feldman. [*Does Learning Require Memorization? A Short Tale about a Long Tail*](https://arxiv.org/pdf/1906.05271). STOC 2020. ### Applied Cryptography - Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish. [*Verifying Computations with State*](https://eprint.iacr.org/2013/356.pdf). SOSP 2013. - Bryan Parno, Jon Howell, Craig Gentry, and Mariana Raykova. [*Pinocchio: Nearly Practical Verifiable Computation*](https://eprint.iacr.org/2013/279.pdf). S&P 2013. - Aseem Rastogi, Matthew A. Hammer and Michael Hicks. [*Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations*](http://www.cs.umd.edu/~aseem/wysteria-tr.pdf). S&P 2014. - Shai Halevi and Victor Shoup. [*Algorithms in HElib*](https://www.shoup.net/papers/helib.pdf). CRYPTO 2014. - Shai Halevi and Victor Shoup. [*Bootstrapping for HElib*](https://www.shoup.net/papers/boot.pdf). EUROCRYPT 2015. - Léo Ducas and Daniele Micciancio. [*FHEW: Bootstrapping Homomorphic Encryption in Less than a Second*](https://eprint.iacr.org/2014/816.pdf). EUROCRYPT 2015. - Peter Kairouz, Sewoong Oh, and Pramod Viswanath. [*Secure Multi-party Differential Privacy*](https://papers.nips.cc/paper/6004-secure-multi-party-differential-privacy.pdf). NIPS 2015. - Arjun Narayan, Ariel Feldman, Antonis Papadimitriou, and Andreas Haeberlen. [*Verifiable Differential Privacy*](https://www.cis.upenn.edu/~ahae/papers/verdp-eurosys2015.pdf). EUROSYS 2015. - Henry Corrigan-Gibbs and Dan Boneh. [*Prio: Private, Robust, and Scalable Computation of Aggregate Statistics*](https://people.csail.mit.edu/henrycg/files/academic/papers/nsdi17prio.pdf). NSDI 2017. - Zahra Ghodsi, Tianyu Gu, Siddharth Garg. [*SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud*](https://arxiv.org/pdf/1706.10268). NIPS 2017. - Valerie Chen, Valerio Pastro, Mariana Raykova. [*Secure Computation for Machine Learning With SPDZ*](https://arxiv.org/pdf/1901.00329). 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*](https://gzs715.github.io/pubs/WATERMARK_ASIACCS18.pdf). 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*](https://arxiv.org/pdf/1802.04633). USENIX 2018. - Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica. [*Helen: Maliciously Secure Coopetitive Learning for Linear Models*](https://arxiv.org/pdf/1907.07212). S&P 2019. - Bita Darvish Rouhani, Huili Chen, and Farinaz Koushanfar. [*DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models*](https://arxiv.org/pdf/1804.00750). 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*](https://dl.acm.org/ft_gateway.cfm?id=3314628&ftid=2065506&dwn=1). PLDI 2019. ### Algorithmic Fairness - Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. [*Fairness through Awarness*](https://arxiv.org/pdf/1104.3913). ITCS 2012. - Moritz Hardt, Eric Price, and Nathan Srebro. [*Equality of Opportunity in Supervised Learning*](https://arxiv.org/pdf/1610.02413). 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*](https://arxiv.org/pdf/1607.06520). 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*](https://arxiv.org/pdf/1707.09457). EMNLP 2017. - Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. [*Inherent Trade-Offs in the Fair Determination of Risk Scores*](https://arxiv.org/pdf/1609.05807). ITCS 2017. - Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. [*Avoiding Discrimination through Causal Reasoning*](https://arxiv.org/pdf/1706.02744). NIPS 2017. - Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva. [*Counterfactual Fairness*](https://arxiv.org/pdf/1703.06856). NIPS 2017. - Razieh Nabi and Ilya Shpitser. [*Fair Inference on Outcomes*](https://arxiv.org/pdf/1705.10378). AAAI 2018. - Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum. [*Multicalibration: Calibration for the (Computationally-Identifiable) Masses*](https://arxiv.org/pdf/1711.08513.pdf). ICML 2018. - Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. [*Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness*](https://arxiv.org/pdf/1711.05144). ICML 2018. - Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. [*A Reductions Approach to Fair Classification*](https://arxiv.org/pdf/1803.02453). ICML 2019. - Ben Hutchinson and Margaret Mitchell. [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://arxiv.org/pdf/1811.10104). FAT\* 2019. ### PL and Verification - Martín Abadi and Andrew D. Gordon. [*A Calculus for Cryptographic Protocols: The Spi Calculus*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ic99spi.pdf). Information and Computation, 1999. - Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum. [*Church: a language for generative models*](https://arxiv.org/pdf/1206.3255). UAI 2008. - Frank McSherry. [*Privacy Integrated Queries*](http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.211.4503). SIGMOD 2009. - Marta Kwiatkowska, Gethin Norman, and David Parker. [*Advances and Challenges of Probabilistic Model Checking*](https://www.prismmodelchecker.org/papers/allerton10.pdf). Allerton 2010. - Jason Reed and Benjamin C. Pierce. [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf). 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*](https://www.usenix.org/system/files/conference/osdi12/osdi12-final-35.pdf). OSDI 2012. - Danfeng Zhang, Aslan Askarov, and Andrew C. Myers. [*Language-Based Control and Mitigation of Timing Channels*](https://www.cs.cornell.edu/andru/papers/pltiming-pldi12.pdf). PLDI 2012. - Andrew Miller, Michael Hicks, Jonathan Katz, and Elaine Shi. [*Authenticated Data Structures, Generically*](https://www.cs.umd.edu/~mwh/papers/gpads.pdf). POPL 2014. - Andrew D. Gordon, Thomas A. Henzinger, Aditya V. Nori, and Sriram K. Rajamani. [*Probabilistic Programming*](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/fose-icse2014.pdf). 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*](https://arxiv.org/pdf/1407.6845.pdf). POPL 2015. - Samee Zahur and David Evans. [*Obliv-C: A Language for Extensible Data-Oblivious Computation*](https://eprint.iacr.org/2015/1153.pdf). IACR 2015. - Chang Liu, Xiao Shaun Wang, Kartik Nayak, Yan Huang, and Elaine Shi. [*ObliVM: A Programming Framework for Secure Computation*](http://www.cs.umd.edu/~elaine/docs/oblivm.pdf). S&P 2015. - Gilles Barthe, Marco Gaboardi, Benjamin Grégoire, Justin Hsu, and Pierre-Yves Strub. [*A Program Logic for Union Bounds*](https://arxiv.org/pdf/1602.05681). ICALP 2016. - Christian Albert Hammerschmidt, Sicco Verwer, Qin Lin, and Radu State. [*Interpreting Finite Automata for Sequential Data*](https://arxiv.org/pdf/1611.07100). NIPS 2016. - Joost-Pieter Katoen. [*The Probabilistic Model Checking Landscape*](https://moves.rwth-aachen.de/wp-content/uploads/lics2016_tutorial_katoen.pdf). 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*](http://www.cse.psu.edu/~dbz5017/pub/asplos17.pdf). ASPLOS 2017. - Frits Vaandrager. [*Model Learning*](https://m-cacm.acm.org/magazines/2017/2/212445-model-learning/fulltext). 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*](https://files.sri.inf.ethz.ch/website/papers/sp2018.pdf). S&P 2018. - Matthew Mirman, Timon Gehr, and Martin Vechev. [*Differentiable Abstract Interpretation for Provably Robust Neural Networks*](http://proceedings.mlr.press/v80/mirman18b/mirman18b.pdf). ICML 2018. - Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. [*Automatic differentiation in machine learning: a survey*](https://arxiv.org/pdf/1502.05767). JMLR 2018. - Gagandeep Singh, Timon Gehr, Markus Püschel, and Martin T. Vechev. [*An Abstract Domain for Certifying Neural Networks*](https://files.sri.inf.ethz.ch/website/papers/DeepPoly.pdf). POPL 2019. - Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, and Martin Vechev. [*DL2: Training and Querying Neural Networks with Logic*](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf). ICML 2019. - Abhinav Verma, Hoang M. Le, Yisong Yue, and Swarat Chaudhuri. [*Imitation-Projected Programmatic Reinforcement Learning*](https://arxiv.org/pdf/1907.05431). NeurIPS 2019. - Kenneth L. McMillan [*Bayesian Interpolants as Explanations for Neural Inferences*](https://arxiv.org/abs/2004.04198). arXiv. # Supplemental Material - Cynthia Dwork and Aaron Roth. [*Algorithmic Foundations of Data Privacy*](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf). - Solon Barocas, Moritz Hardt, and Arvind Narayanan. [*Fairness and Machine Learning: Limitations and Opportunities*](https://fairmlbook.org/index.html). - Gilles Barthe, Marco Gaboardi, Justin Hsu, and Benjamin C. Pierce. [*Programming Language Techniques for Differential Privacy*](https://siglog.hosting.acm.org/wp-content/uploads/2016/01/siglog_news_7.pdf). - Michael Walfish and Andrew J. Blumberg. [*Verifying Computations without Reexecuting Them*](http://delivery.acm.org/10.1145/2650000/2641562/p74-walfish.pdf?ip=24.59.48.254&id=2641562&acc=OA&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E757E42EE4C319386&__acm__=1533144327_267b96b7bd723efc52072f0f79f6720d). - Véronique Cortier, Steve Kremer, and Bogdan Warinschi. [*A Survey of Symbolic Methods in Computational Analysis of Cryptographic Systems*](https://hal.inria.fr/inria-00379776/document). - Dan Boneh and Victor Shoup. [*A Graduate Course in Applied Cryptography*](https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf). - David Hand. [*Statistics and the Theory of Measurement*](http://www.lps.uci.edu/~johnsonk/CLASSES/MeasurementTheory/Hand1996.StatisticsAndTheTheoryOfMeasurement.pdf). - Judea Pearl. [*Causal inference in statistics: An overview*](http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf). - Judea Pearl. [*Understanding Simpson’s Paradox*](https://ftp.cs.ucla.edu/pub/stat_ser/r414.pdf). - Yehuda Lindell and Benny Pinkas. [*Secure Multiparty Computation for Privacy-Preserving Data Mining*](https://eprint.iacr.org/2008/197.pdf).