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With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular.
walk2friends: Inferring Social Links from Mobility Profiles. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1943–1957
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Blind Vision. In Proceedings of the 2006 European Conference on Computer Vision (ECCV) . Springer, 1–13
Shai Avidan and Moshe Butman. 2006 · 2006
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Enhanced Privacy ID from Bilinear Pairing for Hardware Authentication and Attestation. In Proceedings of the 2010 IEEE International Conference on Social Computing (SocialCom) . IEEE, 768–775
Ernie Brickell and Jiangtao Li. 2010 · 2010
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Quantifying Location Privacy. In Proceedings of the 2011 IEEE Symposium on Security and Privacy (S&P) . IEEE, 247–262
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Preserving Link Privacy in Social Network Based Systems. In Proceedings of the 2013 Network and Distributed System Security Symposium (NDSS) . Internet Society
Prateek Mittal, Charalampos Papamanthou, and Dawn Song. 2013 · 2013
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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In Proceedings of the 2014 USENIX Security Symposium (USENIX Security) . USENIX, 17–32
Matt Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014 · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Machine Learning Classification over Encrypted Data. In Proceedings of the 2015 Network and Distributed System Security Symposium (NDSS) . Internet Society
Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser. 2015 · 2015
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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. In Proceedings of the 2015 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 1322–1333
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015 · 2015
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SGX Secure Enclaves in Practice: Security and Crypto Review. In Proceedings of the 2016 Black Hat (Black Hat)
JP Aumasson and Luis Merino. 2016 · 2016
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Foundations of Hardware-Based Attested Computation and Application to SGX. In Proceedings of the 2016 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE, 245–260
Manuel Barbosa, Bernardo Portela, Guillaume Scerri, and Bogdan Warinschi. 2016 · 2016
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Sanctum: Minimal Hardware Extensions for Strong Software Isolation. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 857–874
Victor Costan, Ilia Lebedev, and Srinivas Devadas. 2016 · 2016
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CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. In Proceedings of the 2016 International Conference on Machine Learning (ICML) . JMLR, 201–210
Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
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Oblivious Multi-Party Machine Learning on Trusted Processors. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 619–636
Olga Ohrimenko, Felix Schuster, Cedric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. 2016 · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016 · 2016
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Ariadne: A Minimal Approach to State Continuity. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 875–892
Raoul Strackx and Frank Piessens. 2016 · 2016
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Stealing Machine Learning Models via Prediction APIs. In Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 601–618
Florian Tramér, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016 · 2016
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Software Grand Exposure: SGX Cache Attacks Are Practical. In Proceedings of the 2017 USENIX Workshop on Offensive Technologies (WOOT) . USENIX
Ferdinand Brasser, Urs Muller, Alexandra Dmitrienko, Kari Kostiainen, Srdjan Capkun, and Ahmad-Reza Sadeghi. 2017 · 2017
Cited alongside, same era.
Towards Evaluating the Robustness of Neural Networks. In Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P) . IEEE, 39–57
Nicholas Carlini and David Wagner. 2017 · 2017
Cited alongside, same era.
Iron: Functional Encryption using Intel SGX. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 765–782
Ben Fisch, Dhinakaran Vinayagamurthy, Dan Boneh, and Sergey Gorbunov. 2017 · 2017
Cited alongside, same era.
LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. 2017 · 2017
Cited alongside, same era.
Dissecting Privacy Risks in Biomedical Data. In Proceedings of the 2018 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE
Pascal Berrang, Mathias Humbert, Yang Zhang, Irina Lehmann, Roland Eils, and Michael Backes. 2018 · 2018
Closest in time.
Foreshadow: Extracting the Keys to the Intel SGX Kingdom with Transient Out-of-Order Execution. In Proceedings of the 2018 USENIX Security Symposium (USENIX Security) . USENIX, 991–1008
Jo Van Bulck, Marina Minkin, Ofir Weisse, Daniel Genkin, Baris Kasikci, Frank Piessens, Mark Silberstein, Thomas F. Wenisch, Yuval Yarom, and Raoul Strackx. 2018 · 2018
Closest in time.
Securing Input Data of Deep Learning Inference Systems via Partitioned Enclave Execution
Zhongshu Gu, Heqing Huang, Jialong Zhang, Dong Su, Ankita Lamba, Dimitrios Pendarakis, and Ian Molloy. 2018 · 2018
Closest in time.
Chiron: Privacy-preserving Machine Learning as a Service
Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, and Emmett Witchel. 2018 · 2018
Closest in time.
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Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017 · 2017
Cited alongside, same era.
Safety Verification of Deep Neural Networks. In Proceedings of the 2017 International Conference on Computer Aided Verification (CAV) . Springer, 3–29
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu. 2017 · 2017
Cited alongside, same era.
Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing. In Proceedings of the 2017 USENIX Security Symposium (USENIX Security) . USENIX, 557–574
Sangho Lee, Ming-Wei Shih, Prasun Gera, Taesoo Kim, Hyesoon Kim, and Marcus Peinado. 2017 · 2017
Cited alongside, same era.
Oblivious Neural Network Predictions via MiniONN Transformations. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 619–631
Jian Liu, Mika Juuti, Yao Lu, and N. Asokan. 2017 · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
Cited alongside, same era.
ROTE: Rollback Protection for Trusted Execution. In Proceedings of the 2017 USENIX Security Symposium (USENIX Security) . USENIX, 1289–1306
Sinisa Matetic, Mansoor Ahmed, Kari Kostiainen, Aritra Dhar, David Sommer, Arthur Gervais, Ari Juels, and Srdjan Capkun. 2017 · 2017
Cited alongside, same era.
SecureML: A System for Scalable Privacy-Preserving Machine Learning. In Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P) . IEEE, 19–38
Payman Mohassel and Yupeng Zhang. 2017 · 2017
Cited alongside, same era.
Formal Abstractions for Attested Execution Secure Processors. In Proceedings of the 2017 Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT) . Springer, 260–289
Rafael Pass, Elaine Shi, and Florian Tramér. 2017 · 2017
Cited alongside, same era.
Nick Hynes, Raymond Cheng, and Dawn Song. 2018 · 2018
Closest in time.
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Alexey Dmitrenko, Samuel Marchal, and N. Asokan. 2018 · 2018
Closest in time.
Inference Attacks Against Collaborative Learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2018 · 2018
Closest in time.
Towards Reverse-Engineering Black-Box Neural Networks. In Proceedings of the 2018 International Conference on Learning Representations (ICLR)
Seong Joon Oh, Max Augustin, Bernt Schiele, and Mario Fritz. 2018 · 2018
Closest in time.
Understanding and Controlling User Linkability in Decentralized Learning
Tribhuvanesh Orekondy, Seong Joon Oh, Bernt Schiele, and Mario Fritz. 2018 · 2018
Closest in time.
SoK: Towards the Science of Security and Privacy in Machine Learning. In Proceedings of the 2018 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman. 2018 · 2018
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Certified Defenses against Adversarial Examples. In Proceedings of the 2018 International Conference on Learning Representations (ICLR)
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang. 2018 · 2018
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Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Florian Tramer and Dan Boneh. 2018 · 2018
Closest in time.
Stealing Hyperparameters in Machine Learning. In Proceedings of the 2018 IEEE Symposium on Security and Privacy (S&P) . IEEE
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
Closest in time.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. In Proceedings of the 2018 IEEE Computer Security Foundations Symposium (CSF) . IEEE
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
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Protecting Intellectual Property of Deep Neural Networks with Watermarking. In Proceedings of the 2018 ACM Asia Conference on Computer and Communications Security (ASIACCS) . ACM, 159–172
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph. Stoecklin, Heqing Huang, and Ian Molloy. 2018a · 2018
Closest in time.
Tagvisor: A Privacy Advisor for Sharing Hashtags. In Proceedings of the 2018 Web Conference (WWW) . ACM, 287–296
Yang Zhang, Mathias Humbert, Tahleen Rahman, Cheng-Te Li, Jun Pang, and Michael Backes. 2018b · 2018
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ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Proceedings of the 2019 Network and Distributed System Security Symposium (NDSS) . Internet Society
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2019 · 2019
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