# 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).