8.6 KiB
8.6 KiB
Paper Suggestions
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.
- 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.
- Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner. Local Differential Privacy for Evolving Data.
Adversarial Machine Learning
- 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.
- Nicholas Carlini and David Wagner. Towards Evaluating the Robustness of Neural Networks. S&P 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.
- 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.
- Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards Deep Learning Models Resistant to Adversarial Attacks. ICLR 2018.
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.
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.
- Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS 2017.
- 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.
Programming Languages and Verification
- Martín Abadi and Andrew D. Gordon. A Calculus for Cryptographic Protocols: The Spi Calculus. Information and Computation, 1999.
- Frank McSherry. Privacy Integrated Queries. SIGMOD 2009.
- 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.
- 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.
- 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.
Supplemental Material
- Cynthia Dwork and Aaron Roth. Algorithmic Foundations of Data Privacy.
- 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.