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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  ...  Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private.  ...  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

Fast and Secure Three-party Computation

Payman Mohassel, Mike Rosulek, Ye Zhang
2015 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security - CCS '15  
Our protocol is based on garbled circuits and provides security against a single, malicious corrupt party. Unlike information-theoretic 3PC protocols, ours uses a constant number of rounds.  ...  We demonstrate the practicality of our protocol with an implementation based on the JustGarble framework of Bellare et al. (S&P 2013).  ...  We are also appreciative of Yuval Ishai and Ranjit Kumaresan for bringing some related work to our attention.  ... 
doi:10.1145/2810103.2813705 dblp:conf/ccs/MohasselRZ15 fatcat:jrjvkxkujfh5llgdwjlneykdmu

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.  ...  Acknowledgments We thank Vikash Sehwag for his help with the experiments, the anonymous reviews, and our Shepherd Melek Önen.  ... 
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.  ...  Acknowledgments We thank Vikash Sehwag for his help with the experiments, the anonymous reviews, and our Shepherd Melek Önen.  ... 
doi:10.2478/popets-2021-0011 fatcat:of622d63gzggthhd2avph3l2yi

Machine-checked ZKP for NP-relations: Formally Verified Security Proofs and Implementations of MPC-in-the-Head [article]

José Carlos Bacelar Almeida
2021 arXiv   pre-print
MPC-in-the-Head (MitH) is a general framework that allows constructing efficient Zero Knowledge protocols for general NP-relations from secure multiparty computation (MPC) protocols.  ...  Using a recently developed code extraction mechanism for EasyCrypt we synthesize a formally verified implementation of the protocol, which we benchmark to get an indication of the overhead associated with  ...  This means that general feasibility results for two-party secure computation-where no honest majority can be assumed and malicious behavior must be considered-translate into feasibility results for ZK  ... 
arXiv:2104.05516v3 fatcat:uytuhvwdxratpel7fh3etledji

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation [article]

Siddharth Garg, Zahra Ghodsi, Carmit Hazay, Yuval Ishai, Antonio Marcedone, Muthuramakrishnan Venkitasubramaniam
2018 arXiv   pre-print
Secure neural networks based classification algorithms can be seen as an instantiation of an arithmetic computation over integers.  ...  In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations.  ...  [WGC18] , the authors introduce SecureNN, a tool for training and predication in the three-party and four-party settings with honest majority.  ... 
arXiv:1812.01372v1 fatcat:tdxijdfz2vei3hefs2lolyubyq

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

Henry Corrigan-Gibbs, Dan Boneh
2017 arXiv   pre-print
Each Prio client holds a private data value (e.g., its current location), and a small set of servers compute statistical functions over the values of all clients (e.g., the most popular location).  ...  This paper presents Prio, a privacy-preserving system for the collection of aggregate statistics.  ...  C.1 Definition: Arithmetic circuits An arithmetic circuit C over a finite field F takes as input a vector x = x (1) , . . . , x (L) ∈ F L and produces a single field element as output.  ... 
arXiv:1703.06255v1 fatcat:hcfefepsuvdjnbhdnpmaizcpmu

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  ...  This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC.  ...  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  
Our main contribution is a hybrid multi-party computation protocol that combines Yao's garbled circuits with tailored protocols for computing inner products.  ...  This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained  ...  We implemented and evaluated the garbled circuit with malicious security for this verification phase, as an extension for our solving phase, using the EMP framework [67] .  ... 
doi:10.1515/popets-2017-0053 dblp:journals/popets/GasconSB0DZE17 fatcat:hpn4a3ulf5dstojfrvjesrjf6y

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning [article]

Rahul Rachuri, Ajith Suresh
2019 arXiv   pre-print
Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning.  ...  We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds.  ...  Our Contribution We propose an efficient framework for mixed world computations in the four-party honest majority setting with active security over the ring Z 2 ℓ .  ... 
arXiv:1912.02631v1 fatcat:6hefhsu36vcgxbecma2d5rp5gy

ASTRA

Harsh Chaudhari, Ashish Choudhury, Arpita Patra, Ajith Suresh
2019 Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop - CCSW'19  
In this work, we present concretely-efficient protocols for secure 3-party computation (3PC) over a ring of integers modulo 2^ℓ tolerating one corruption, both with semi-honest and malicious security.  ...  Our constructions catering to both semi-honest and the malicious world, invariably perform better than the existing constructions.  ...  We would like to thank Thomas Schneider for helpful discussions, comments, and pointers.  ... 
doi:10.1145/3338466.3358922 dblp:conf/ccs/ChaudhariCPS19 fatcat:cocumqracfay5ft42yo7myhklm

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  
Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks.  ...  Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use.  ...  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

Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment [article]

Mahnush Movahedi, Benjamin M. Case, Andrew Knox, James Honaker, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck
2021 arXiv   pre-print
The second stage runs multiple instances of a general purpose MPC over a sharded database to aggregate statistics about each experimental group while discarding individuals who took an action before they  ...  We also evaluate the performance of multiple open source general purpose MPC libraries for this task.  ...  For arithmetic circuits over the integers, the construction results in garbled circuits with free addition, weighted threshold gates with cost independent of fan-in, and exponentiation by a fixed exponent  ... 
arXiv:2101.04766v2 fatcat:7qvkufa3jbft3lt6jvz7kgdbvu

SoK: Privacy-Preserving Computation Techniques for Deep Learning

José Cabrero-Holgueras, Sergio Pastrana
2021 Proceedings on Privacy Enhancing Technologies  
of integration with DL frameworks commonly used by the data science community.  ...  Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences.  ...  Acknowledgments We thank the anonymous reviewers and our shepherd, Phillipp Schoppmann, for their valuable feedback. We also thank Alberto Di Meglio, Marco Manca  ... 
doi:10.2478/popets-2021-0064 fatcat:hb3kdruxozbspnowy63gynuapy

FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning

Megha Byali, Harsh Chaudhari, Arpita Patra, Ajith Suresh
2020 Proceedings on Privacy Enhancing Technologies  
Assuming a minimal network of pair-wise private channels, we propose an efficient four-party PPML framework over rings ℤ2ℓ, FLASH, the first of its kind in the regime of PPML framework, that achieves the  ...  All the protocols are implemented over a 64-bit ring in LAN and WAN.  ...  Acknowledgment: We thank Ananth Raghunathan, Yupeng Zhang and the anonymous reviewers of PETS 2020 for their valuable comments, which helped us improve the paper.  ... 
doi:10.2478/popets-2020-0036 fatcat:mbsosxrvjzhkjoc6jfvykhegau
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