Paper Suggestions
Differential Privacy
Frank McSherry and Kunal Talwar.
Mechanism Design via Differential Privacy
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy Rothblum.
Differential Privacy under Continual Observation
T.-H. Hubert Chan, Elaine Shi, and Dawn Song.
Private and Continual Release of Statistics
Moritz Hardt, Katrina Ligett, and Frank McSherry.
A Simple and Practical Algorithm for Differentially Private Data Release
Daniel Kifer and Ashwin Machanavajjhala.
A Rigorous and Customizable Framework for Privacy
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova.
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Cynthia Dwork, Moni Naor, Omer Reingold, and Guy N. Rothblum.
Pure Differential Privacy for Rectangle Queries via Private Partitions
Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner.
Local Differential Privacy for Evolving Data
Applied Cryptography
Benjamin Braun, Ariel J. Feldman, Zuocheng Ren, Srinath Setty, Andrew J. Blumberg, and Michael Walfish.
Verifying Computations with State
Bryan Parno, Jon Howell, Craig Gentry, and Mariana Raykova.
Pinocchio: Nearly Practical Verifiable Computation
Aseem Rastogi, Matthew A. Hammer and Michael Hicks.
Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations
Shai Halevi and Victor Shoup.
Algorithms in HElib
Shai Halevi and Victor Shoup.
Bootstrapping for HElib
Léo Ducas and Daniele Micciancio.
FHEW: Bootstrapping Homomorphic Encryption in Less than a Second
Peter Kairouz, Sewoong Oh, and Pramod Viswanath.
Secure Multi-party Differential Privacy
Arjun Narayan, Ariel Feldman, Antonis Papadimitriou, and Andreas Haeberlen.
Verifiable Differential Privacy
Language-Based Security
Martín Abadi and Andrew D. Gordon.
A Calculus for Cryptographic Protocols: The Spi Calculus
Frank McSherry.
Privacy Integrated Queries
Jason Reed and Benjamin C. Pierce.
Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy
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
Danfeng Zhang, Aslan Askarov, and Andrew C. Myers.
Language-Based Control and Mitigation of Timing Channels
Andrew Miller, Michael Hicks, Jonathan Katz, and Elaine Shi.
Authenticated Data Structures, Generically
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
Samee Zahur and David Evans.
Obliv-C: A Language for Extensible Data-Oblivious Computation
Chang Liu, Xiao Shaun Wang, Kartik Nayak, Yan Huang, and Elaine Shi.
ObliVM: A Programming Framework for Secure Computation
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
Adversarial Machine Learning
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus.
Intriguing Properties of Neural Networks
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy.
Explaining and Harnessing Adversarial Examples
Nicholas Carlini and David Wagner.
Towards Evaluating the Robustness of Neural Networks
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
Nicholas Carlini and David Wagner.
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
Jacob Steinhardt, Pang Wei Koh, and Percy Liang.
Certified Defenses for Data Poisoning Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu.
Towards Deep Learning Models Resistant to Adversarial Attacks
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