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In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems.
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints
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Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance
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Universal Circuits (Preliminary Report). In STOC’76
Leslie G Valiant. 1976 · 1976
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How to Generate and Exchange Secrets. In FOCS’86
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Cryptanalytic Extraction of Neural Network Models
Nicholas Carlini, Matthew Jagielski, and Ilya Mironov. 2020 · 2003
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Fairplay — A Secure Two-Party Computation System
Dahlia Malkhi, Noam Nisan, Benny Pinkas, and Yaron Sella. 2004 · 2004
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Prive-HD: Privacy-Preserved Hyperdimensional Computing
Behnam Khaleghi, Mohsen Imani, and Tajana Rosing. 2020 · 2005
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Calibrating Noise to Sensitivity in Private Data Analysis. In Theory of Cryptography
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006 · 2006
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A Practical Universal Circuit Construction and Secure Evaluation of Private Functions. In FC’08
Vladimir Kolesnikov and Thomas Schneider. 2008 · 2008
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Generalized Universal Circuits for Secure Evaluation of Private Functions with Application to Data Classification. In ICISC’08
Ahmad-Reza Sadeghi and Thomas Schneider. 2008 · 2008
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Netflix Prize
2009 · 2009
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Secure Evaluation of Private Linear Branching Programs with Medical Applications. In ESORICS’09
Mauro Barni, Pierluigi Failla, Vladimir Kolesnikov, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, and Thomas Schneider. 2009 · 2009
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A Fully Homomorphic Encryption Scheme
Craig Gentry. 2009 · 2009
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft. 2009 · 2009
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TASTY: Tool for Automating Secure Two-party Computations. In CCS’10
Wilko Henecka, Stefan Kögl, Ahmad-Reza Sadeghi, Thomas Schneider, and Immo Wehrenberg. 2010 · 2010
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Privacy by Design: Delivering the Promises
Peter Hustinx. 2010 · 2010
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MNIST Handwritten Digit Database
Yann LeCun and Corinna Cortes. 2010 · 2010
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Privacy-Preserving ECG Classification with Branching Programs and Neural Networks
Mauro Barni, Pierluigi Failla, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, and Thomas Schneider. 2011 · 2011
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Fully Homomorphic Encryption without Bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. 2011 · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011 · 2011
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Secure Outsourced Computation in a Multi-Tenant Cloud. In IBM Workshop on Cryptography and Security in Clouds
Seny Kamara and Mariana Raykova. 2011 · 2011
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Somewhat Practical Fully Homomorphic Encryption
Junfeng Fan and Frederik Vercauteren. 2012 · 2012
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On reliability Trojan injection and detection
Aswin Sreedhar, Sandip Kundu, and Israel Koren. 2012 · 2012
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Health Insurance Portability and Accountability Act (HIPAA)
2013 · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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Stochastic gradient descent with differentially private updates. In 2013 IEEE Global Conference on Signal and Information Processing . IEEE, 245–248
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate. 2013 · 2013
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Near-data processing: Insights from a MICRO-46 workshop
Rajeev Balasubramonian, Jichuan Chang, Troy Manning, Jaime H Moreno, Richard Murphy, Ravi Nair, and Steven Swanson. 2014 · 2014
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Private empirical risk minimization: Efficient algorithms and tight error bounds. In FOCS’14
Raef Bassily, Adam Smith, and Abhradeep Thakurta. 2014 · 2014
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FHEW: Bootstrapping Homomorphic Encryption in less than a second
Léo Ducas and Daniele Micciancio. 2014 · 2014
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The CIFAR-10 Dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. 2014 · 2014
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ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In NDSS’15
Daniel Demmler, Thomas Schneider, and Michael Zohner. 2015 · 2015
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Vortex: Variation-aware training for memristor X-bar. In DAC’15
B. Liu, Hai Li, Yiran Chen, Xin Li, Qing Wu, and Tingwen Huang. 2015 · 2015
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TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits. In S&P’15
E. M. Songhori, S. U. Hussain, A. Sadeghi, T. Schneider, and F. Koushanfar. 2015 · 2015
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General Data Protection Regulatory (GDPR)
2016 · 2016
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Deep learning with differential privacy. In CCS’16 . 308–318
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds. In ASIACRYPT’16
Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachene. 2016 · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In ICML’16
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
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Valiant’s Universal Circuit is Practical. In EUROCRYPT’16
Ágnes Kiss and Thomas Schneider. 2016 · 2016
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"Why should I trust you?" Explaining the predictions of any classifier. In SIGKDD’16
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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ISAAC: A convolutional neural network accelerator with in-situ analog arithmetic in crossbars
Ali Shafiee, Anirban Nag, Naveen Muralimanohar, Rajeev Balasubramonian, John Paul Strachan, Miao Hu, R Stanley Williams, and Vivek Srikumar. 2016 · 2016
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Stealing machine learning models via prediction APIs. In USENIX Security’16
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. 2016 · 2016
Cited alongside, same era.
Accelerator-friendly neural-network training: Learning variations and defects in RRAM crossbar. In DATE’17
Lerong Chen, Jiawen Li, Yiran Chen, Qiuping Deng, Jiyuan Shen, Xiaoyao Liang, and Li Jiang. 2017 · 2017
Cited alongside, same era.
Pushing the Communication Barrier in Secure Computation using Lookup Tables. In NDSS’17
Ghada Dessouky, Farinaz Koushanfar, Ahmad-Reza Sadeghi, Thomas Schneider, Shaza Zeitouni, and Michael Zohner. 2017 · 2017
Cited alongside, same era.
More Efficient Universal Circuit Constructions. In ASIACRYPT’17
Daniel Günther, Ágnes Kiss, and Thomas Schneider. 2017 · 2017
Cited alongside, same era.
Computation-oriented fault-tolerance schemes for RRAM computing systems. In ASP-DAC’17 . IEEE, 794–799
Wenqin Huangfu, Lixue Xia, Ming Cheng, Xiling Yin, Tianqi Tang, Boxun Li, Krishnendu Chakrabarty, Yuan Xie, Yu Wang, and Huazhong Yang. 2017 · 2017
Cited alongside, same era.
Efficient MPC via Program Analysis: A Framework for Efficient Optimal Mixing. In CCS’19
Muhammad Ishaq, Ana L. Milanova, and Vassilis Zikas. 2019 · 2019
Later among the works it cites.
SoK: Modular and Efficient Private Decision Tree Evaluation
Ágnes Kiss, Masoud Naderpour, Jian Liu, N Asokan, and Thomas Schneider. 2019 · 2019
Later among the works it cites.
Improving noise tolerance of mixed-signal neural networks. In IJCNN’19 . IEEE, 1–8
Michael Klachko, Mohammad Reza Mahmoodi, and Dmitri Strukov. 2019 · 2019
Later among the works it cites.
Unmasking Clever Hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2019 · 2019
Later among the works it cites.
Mitigating Reverse Engineering Attacks on Deep Neural Networks. In ISVLSI’19 . 657–662
Y. Liu, D. Dachman-Soled, and A. Srivastava. 2019 · 2019
Later among the works it cites.
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
SecureML: A System for Scalable Privacy-preserving Machine Learning. In S&P’17
Payman Mohassel and Yupeng Zhang. 2017 · 2017
Cited alongside, same era.
Fault and Error Tolerance in Neural Networks: A Review
C. Torres-Huitzil and B. Girau. 2017 · 2017
Cited alongside, same era.
Fault-tolerant training with on-line fault detection for RRAM-based neural computing systems. In DAC’17
Lixue Xia, Mengyun Liu, Xuefei Ning, Krishnendu Chakrabarty, and Yu Wang. 2017 · 2017
Cited alongside, same era.
California Consumer Privacy Act (CCPA)
2018 · 2018
Cited alongside, same era.
Prediction Machines: The Simple Economics of Artificial Intelligence
Avi Goldfarb Ajay Agrawal, Joshua Gans. 2018 · 2018
Cited alongside, same era.
Homomorphic Encryption Security Standard
Martin Albrecht, Melissa Chase, Hao Chen, Jintai Ding, Shafi Goldwasser, Sergey Gorbunov, Shai Halevi, Jeffrey Hoffstein, Kim Laine, Kristin Lauter, Satya Lokam, Daniele Micciancio, Dustin Moody, Travis Morrison, Amit Sahai, and Vinod Vaikuntanathan. 2018 · 2018
Cited alongside, same era.
Clare Naden. 2019 · 2019
Later among the works it cites.
Bit-flip attack: Crushing neural network with progressive bit search. In Proceedings of the IEEE International Conference on Computer Vision . 1211–1220
Adnan Siraj Rakin, Zhezhi He, and Deliang Fan. 2019 · 2019
Later among the works it cites.
Graph Compilers for Artifical Intelligence Training and Inference
Karthee Sivalingam and Nina Mujkanovic. 2019 · 2019
Later among the works it cites.
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware. In ICLR’19
Florian Tramèr and Dan Boneh. 2019 · 2019
Later among the works it cites.
After criticism, Homeland Security drops plans to expand airport face recognition scans to US citizens
Zack Whittaker. 2019 · 2019
Later among the works it cites.
Electronics Supply Chain Integrity Enabled by Blockchain
Xiaolin Xu, Fahim Rahman, Bicky Shakya, Apostol Vassilev, Domenic Forte, and Mark Tehranipoor. 2019 · 2019
Later among the works it cites.
Valiant’s Universal Circuits Revisited: An Overall Improvement and a Lower Bound. In ASIACRYPT’19
Shuoyao Zhao, Yu Yu, Jiang Zhang, and Hanlin Liu. 2019 · 2019
Later among the works it cites.
https://www.inpher.io/news/2020/5/26/named-in-gartner-homomorphic-encryption-report
2020 · 2020
Closest in time.
Efficient and Scalable Universal Circuits
Masaud Y. Alhassan, Daniel Günther, Ágnes Kiss, and Thomas Schneider. 2020 · 2020
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Offline Model Guard: Secure and Private ML on Mobile Devices. In DATE’20
Sebastian P. Bayerl, Tommaso Frassetto, Patrick Jauernig, Korbinian Riedhammer, Ahmad-Reza Sadeghi, Thomas Schneider, Emmanuel Stapf, and Christian Weinert. 2020 · 2020
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MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference. In ARES’20
Fabian Boemer, Rosario Cammarota, Daniel Demmler, Thomas Schneider, and Hossein Yalame. 2020 · 2020
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Microsoft Urged To Follow Amazon And IBM: Stop Selling Facial Recognition To Cops After George Floyd’s Death
Thomas Brewster. 2020 · 2020
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Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O’Keefe, Mark Koren, Théo Ryffel, JB Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, and Markus Anderljung. 2020 · 2020
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EVA: An encrypted vector arithmetic language and compiler for efficient homomorphic computation. In PLDI’20
Roshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai, Kim Laine, and Madan Musuvathi. 2020 · 2020
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FDA Authorizes Marketing of First Cardiac Ultrasound Software That Uses Artificial Intelligence to Guide User
Food and Drug Administration (FDA). 2020 · 2020
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A >100 Gbps Inline AES-GCM Hardware Engine and Protected DMA Transfers between SGX Enclave and FPGA Accelerator Device
Santosh Ghosh, Luis S Kida, Soham Jayesh Desai, and Reshma Lal. 2020 · 2020
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A systematic comparison of encrypted machine learning solutions for image classification. In Proceedings of the 2020 workshop on privacy-preserving machine learning in practice . 55–59
Veneta Haralampieva, Daniel Rueckert, and Jonathan Passerat-Palmbach. 2020 · 2020
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High Accuracy and High Fidelity Extraction of Neural Networks. In USENIX Security’20 . USENIX Association, Boston, MA
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot. 2020 · 2020
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CrypTFlow: Secure TensorFlow Inference. In S&P’20
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma. 2020 · 2020
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DELPHI: A Cryptographic Inference Service for Neural Networks. In USENIX Security’20
Pratyush Mishra, Ryan Lehmkuhl, Akshayaram Srinivasan, Wenting Zheng, and Raluca Ada Popa. 2020 · 2020
Closest in time.
CryptoPIM: In-memory Acceleration for Lattice-based Cryptographic Hardware
Hamid Nejatollahi, Saransh Gupta, Mohsen Imani, Tajana Simunic Rosing, Rosario Cammarota, and Nikil Dutt. 2020 · 2020
Closest in time.
Achieving Trustworthy AI with Standards
Antoinette Price. 2020 · 2020
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Trusted And Assured MicroElectronics (TAME) forum: Working Groups Report
Mark Tehranipoor, Waleed Khalil, Matthew Casto, Yousef Iskander, Brian Dupaix, Rosario Cammarota, and Brian Cohen. 2019 · 2020
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CryptoSPN: Privacy-preserving Sum-Product Network Inference. In ECAI’20
Amos Treiber, Alejandro Molina, Christian Weinert, Thomas Schneider, and Kristian Kersting. 2020 · 2020
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Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures. In USENIX Security’20
Mengjia Yan, Christopher W. Fletcher, and Josep Torrellas. 2020 · 2020
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Exploring design and governance challenges in the development of privacy-preserving computation. In CHI’21
Nitin Agrawal, Reuben Binns, Max Van Kleek, Kim Laine, and Nigel Shadbolt. 2021 · 2021
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Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks. In Cyber Security Cryptography and Machine Learning . Springer
Ilaria Chillotti, Marc Joye, and Pascal Paillier. 2021 · 2021
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Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)
European Commission. 2021 · 2021
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Pushing the Limits of Valiant’s Universal Circuits: Simpler, Tighter and More Compact. In CRYPTO’21
Hanlin Liu, Yu Yu, Shuoyao Zhao, Jiang Zhang, Wenling Liu, and Zhenkai Hu. 2021 · 2021
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Three halves make a whole? Beating the half-gates lower bound for garbled circuits. In CRYPTO’21
Mike Rosulek and Lawrence Roy. 2021 · 2021
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Partial Dependence and Individual Conditional Expectation plots
2007–2022 · 2022
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OpenFHE: Open-Source Fully Homomorphic Encryption Library
Ahmad Al Badawi, Jack Bates, Flavio Bergamaschi, David Bruce Cousins, Saroja Erabelli, Nicholas Genise, Shai Halevi, Hamish Hunt, Andrey Kim, Yongwoo Lee, Zeyu Liu, Daniele Micciancio, Ian Quah, Yuriy Polyakov, Saraswathy R.V., Kurt Rohloff, Jonathan Saylor, Dmitriy Suponitsky, Matthew Triplett, Vinod Vaikuntanathan, and Vincent Zucca. 2022 · 2022
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Responsible AI investments and safeguards for facial recognition
Sarah Bird. 2022 · 2022
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MOTION – A Framework for Mixed-Protocol Multi-Party Computation
Lennart Braun, Daniel Demmler, Thomas Schneider, and Oleksandr Tkachenko. 2022 · 2022
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XORBoost: Tree Boosting in the Multiparty Computation Setting
Kevin Deforth, Marc Desgroseilliers, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev, and Marius Vuille. 2022 · 2022
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Rising premiums, more restricted cyber insurance coverage poses big risk for companies
Bob Violino. 2022 · 2022
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