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Fast Large-Scale Honest-Majority MPC for Malicious Adversaries [chapter]

Koji Chida, Daniel Genkin, Koki Hamada, Dai Ikarashi, Ryo Kikuchi, Yehuda Lindell, Ariel Nof
2018 Lecture Notes in Computer Science  
Our protocols are information-theoretically secure in the presence of a malicious adversaries, assuming an honest majority.  ...  The two classic adversary models considered are semi-honest (where the adversary follows the protocol specification but tries to learn more than allowed by examining the protocol transcript) and malicious  ...  Our protocols are information-theoretically secure in the presence of a malicious adversaries, assuming an honest majority.  ... 
doi:10.1007/978-3-319-96878-0_2 fatcat:55tehzjfi5h3ba5ot5ckt5fj7a

FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning [article]

Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, Tal Rabin
2020 arXiv   pre-print
such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make  ...  Our experiments in the WAN setting show that over large networks and datasets, compute operations dominate the overall latency of MPC, as opposed to the communication.  ...  We also thank the following grant/awards for supporting this work: Facebook  ... 
arXiv:2004.02229v2 fatcat:gyp7wvpezvb2nkdcqnnj4sspn4

Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning

Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, Tal Rabin
2021 Proceedings on Privacy Enhancing Technologies  
such as AlexNet (iii) Falcon guarantees security with abort against malicious adversaries, assuming an honest majority (iv) Lastly, Falcon presents new theoretical insights for protocol design that make  ...  Our experiments in the WAN setting show that over large networks and datasets, compute operations dominate the overall latency of MPC, as opposed to the communication.  ...  We also thank the following grant/awards for supporting this work: Facebook  ... 
doi:10.2478/popets-2021-0011 fatcat:of622d63gzggthhd2avph3l2yi

Conclave

Nikolaj Volgushev, Malte Schwarzkopf, Ben Getchell, Mayank Varia, Andrei Lapets, Azer Bestavros
2019 Proceedings of the Fourteenth EuroSys Conference 2019 CD-ROM on ZZZ - EuroSys '19  
Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use.  ...  Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks.  ...  Acknowledgements We thank Ran Canetti, Tore Kasper Frederiksen, Derek Leung, and Nickolai Zeldovich for their helpful feedback on drafts of this paper.  ... 
doi:10.1145/3302424.3303982 dblp:conf/eurosys/VolgushevSGVLB19 fatcat:xn7ichau3ndr5kfmvdqkp5wfk4

Helen: Maliciously Secure Coopetitive Learning for Linear Models [article]

Wenting Zheng, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica
2019 arXiv   pre-print
Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m-1 out of m parties.  ...  Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection).  ...  Acknowledgment We thank the anonymous reviewers for their valuable reviews, as well as Shivaram Venkataraman, Stephen Tu, and Akshayaram Srinivasan for their feedback and discussions.  ... 
arXiv:1907.07212v2 fatcat:go2j2nbfyjbx7d62wydrag5hmq

Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors [article]

Timothy Stevens, Christian Skalka, Christelle Vincent, John Ring, Samuel Clark, Joseph Near
2021 arXiv   pre-print
Our protocol outperforms current state-of-the art techniques, and empirical results show that it scales to a large number of parties, with optimal accuracy for any differentially private federated learning  ...  Recent advances in secure aggregation using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale.  ...  Furthermore, MPC protocols must assume that some proportion of the involved clients are honest. FLDP assumes an honest majority against a malicious adversary.  ... 
arXiv:2112.06872v1 fatcat:k2cbkzojcvfybabfhzttk6gk2i

Recent Advances in Practical Secure Multi-Party Computation

Satsuya OHATA
2020 IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences  
Then, we show and discuss current situations on higher-level secure protocols, privacy-preserving data analysis, and frameworks/compilers for implementing MPC applications with low-cost. key words: secure  ...  In this paper, we introduce the basic matters on MPC and show recent practical advances. We first explain the settings, security notions, and cryptographic building blocks of MPC.  ...  In this setting, the rate of malicious parties affects the effi- ciency of MPC. In recent situations, there are many practical schemes in semi-honest or honest-majority settings.  ... 
doi:10.1587/transfun.2019dmi0001 fatcat:zw747hbb2vfpfl3jdpti4tdeum

Scaling Private Set Intersection to Billion-Element Sets [chapter]

Seny Kamara, Payman Mohassel, Mariana Raykova, Saeed Sadeghian
2014 Lecture Notes in Computer Science  
Our protocols are secure in several adversarial models including against a semi-honest, covert and malicious server; and address a range of security and privacy concerns including fairness and the leakage  ...  Unfortunately, the most efficient constructions only scale to sets containing a few thousand elementseven in the semi-honest model and over a LAN.  ...  Optimizations for large sets.  ... 
doi:10.1007/978-3-662-45472-5_13 fatcat:agyoneak6vexlmio7ee6z65ut4

Privacy Preserving and Resilient RPKI [article]

Kris Shrishak, Haya Shulman
2021 arXiv   pre-print
However, RPKI enables Regional Internet Registries (RIRs) to unilaterally takedown IP prefixes - indeed, such attacks have been launched by nation-state adversaries.  ...  An adversary can be honest-but-curious or malicious.  ...  An honest-but-curious adversary follows the protocol while a malicious adversary does not follow the protocol and might actively disrupt the protocol.  ... 
arXiv:2102.02456v1 fatcat:dprilk34xncc5i7e6chqwptfhy

Optimizing Semi-Honest Secure Multiparty Computation for the Internet

Aner Ben-Efraim, Yehuda Lindell, Eran Omri
2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16  
Our first protocol uses oblivious transfer and constitutes the first concretely-efficient constant-round multiparty protocol for the case of no honest majority.  ...  In this paper, we construct highly efficient constant-round protocols for the setting of multiparty computation for semihonest adversaries.  ...  Acknowledgements We sincerely thank Meital Levy, Assi Barak, Shay Gueron, and Roi Inbar for their help on the implementations and experiments in this paper.  ... 
doi:10.1145/2976749.2978347 dblp:conf/ccs/Ben-EfraimLO16 fatcat:pdeycn26lrcmnbv7erkaqnouqq

Enigma: Decentralized Computation Platform with Guaranteed Privacy [article]

Guy Zyskind, Oz Nathan, Alex Pentland
2015 arXiv   pre-print
For storage, we use a modified distributed hashtable for holding secret-shared data.  ...  For the first time, users are able to share their data with cryptographic guarantees regarding their privacy.  ...  A protocol secure against malicious adversaries (with dishonest majority), providing correctness guarantees for MPC.  ... 
arXiv:1506.03471v1 fatcat:gcrwidph25edhjr2kzwllzcpsa

Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities

Joseph I. Choi, Kevin R. B. Butler
2019 Security and Communication Networks  
This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation  ...  Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable.  ...  Acknowledgments Special thanks are due to Patrick Traynor and Thomas Shrimpton for their interest in and constructive criticisms of this work.  ... 
doi:10.1155/2019/1368905 fatcat:izynm6msrvehfa3ghkw7tykk34

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, David Evans
2017 Proceedings on Privacy Enhancing Technologies  
s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses.  ...  Our main contribution is a hybrid multi-party computation protocol that combines Yao's garbled circuits with tailored protocols for computing inner products.  ...  -A fast solver for high-dimensional linear systems suitable for MPC (Section 5).  ... 
doi:10.1515/popets-2017-0053 dblp:journals/popets/GasconSB0DZE17 fatcat:hpn4a3ulf5dstojfrvjesrjf6y

Prio: Private, Robust, and Scalable Computation of Aggregate Statistics [article]

Henry Corrigan-Gibbs, Dan Boneh
2017 arXiv   pre-print
Prio extends classic private aggregation techniques to enable the collection of a large class of useful statistics.  ...  This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics.  ...  This technique scales very well and is robust even if some of the phones are malicious-each phone can influence the final sum by ±1 at most.  ... 
arXiv:1703.06255v1 fatcat:hcfefepsuvdjnbhdnpmaizcpmu

Atom: Horizontally Scaling Strong Anonymity [article]

Albert Kwon, Henry Corrigan-Gibbs, Srinivas Devadas, Bryan Ford
2017 arXiv   pre-print
Atom is most suitable for sending a large number of short messages, as in a microblogging application or a high-security communication bootstrapping ("dialing") for private messaging systems.  ...  At the same time, each Atom user benefits from "best possible" anonymity: a user is anonymous among all honest users of the system, against an active adversary who controls the entire network, a portion  ...  Acknowledgements We thank Nirvan Tyagi, David Lazar, Riad Wahby, Ling Ren, and Dan Boneh for valuable feedback and discussion during this project.  ... 
arXiv:1612.07841v3 fatcat:6bvpcxo3sfh4vmftukvhtvs5dq
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