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Fast Large-Scale Honest-Majority MPC for Malicious Adversaries
[chapter]
2018
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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
2021
Proceedings on Privacy Enhancing Technologies
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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
2019
Proceedings of the Fourteenth EuroSys Conference 2019 CD-ROM on ZZZ - EuroSys '19
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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]
2019
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2020
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
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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]
2014
Lecture Notes in Computer Science
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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]
2021
arXiv
pre-print
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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
2016
Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16
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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]
2015
arXiv
pre-print
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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
2019
Security and Communication Networks
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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
2017
Proceedings on Privacy Enhancing Technologies
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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]
2017
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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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