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High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority

Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, Kazuma Ohara
2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16  
In this paper, we describe a new information-theoretic protocol (and a computationally-secure variant) for secure threeparty computation with an honest majority.  ...  Our work demonstrates that high-throughput secure computation is possible on standard hardware.  ...  Related Work We compare our results with previously reported results on secure AES computation for 3 parties with an honest majority and semi-honest adversaries; see Table 1 .  ... 
doi:10.1145/2976749.2978331 dblp:conf/ccs/ArakiFLNO16 fatcat:h5tasgt7mzefrocw2zzq7jvgey

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.  ...  In the semi-honest setting, our protocol requires communication of 2 ring elements per multiplication gate during the online phase, attaining a per-party cost of less than one element.  ...  OUR 3PC PROTOCOL We start with our 3PC protocol Π s 3pc that securely evaluates any arithmetic circuit over Z 2 ℓ for ℓ ≥ 1, tolerating semi-honest adversaries. 3PC with semi-honest security Our protocol  ... 
doi:10.1145/3338466.3358922 dblp:conf/ccs/ChaudhariCPS19 fatcat:cocumqracfay5ft42yo7myhklm

High-Throughput Secure Three-Party Computation for Malicious Adversaries and an Honest Majority [chapter]

Jun Furukawa, Yehuda Lindell, Ariel Nof, Or Weinstein
2017 Lecture Notes in Computer Science  
In this paper, we describe a new protocol for secure threeparty computation of any functionality, with an honest majority and a malicious adversary.  ...  We start from the recent semi-honest protocol of Araki et al.  ...  Our Results In this paper, we focus on the question of achieving secure computation in the presence of malicious adversaries with very high throughput on a fast network (without utilizing special-purpose  ... 
doi:10.1007/978-3-319-56614-6_8 fatcat:pfnbs3o27fauxdjszi7tykvh2q

DEMO

Toshinori Araki, Assaf Barak, Jun Furukawa, Yehuda Lindell, Ariel Nof, Kazuma Ohara
2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16  
The paper "High Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority" in this ACM CCS 2016 [4] presents a new protocol which its implementation carried out over 1,300,000 AESs  ...  The design will show how this high-throughput three-party computation can be done using simple servers.  ...  INTRODUCTION The authors of this poster proposed, in their paper [4] at this ACM CCS 2016, a novel provably secure three-party computation protocol with honest majority.  ... 
doi:10.1145/2976749.2989035 dblp:conf/ccs/ArakiBFLNO16 fatcat:wb2ga5nyv5hutldzzk6v3wydqe

A SURVEY ON PROTECTING USER PRIVACY OF RELATIONAL DATA USING KNN CLASSIFIER

Abirami. A, Chandrakala. D
2016 ELK Asia Pacific Journal of Computer Science and Information Systems  
KEYWORDS: Security, K-NN classifier, outsourced databases, encryption.  ...  INTRODUCTION: Data mining is the subfield of computer science and is the computational process of discovering patterns in large data sets. It is said to be the analysis step of discovering knowledge.  ...  The protocol used is honest-but-curious model suitable only with three computing participants and fails for greater than three participants and also honest-but-curious model does not hold good  ... 
doi:10.16962/eapjcsis/issn.2394-0441/20160930.v2i1.01 fatcat:jatloegoufe6xnmuwmwdfn6xxi

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

Megha Byali, Harsh Chaudhari, Arpita Patra, Ajith Suresh
2020 Proceedings on Privacy Enhancing Technologies  
AbstractPrivacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has gained momentum in the recent past.  ...  strongest security notion of Guaranteed Output Delivery (all parties obtain the output irrespective of adversary's behaviour).  ...  The remaining parties can then run a semi-honest three-party protocol to compute the output. A similar idea follows for the case when the dealer is an evaluator.  ... 
doi:10.2478/popets-2020-0036 fatcat:mbsosxrvjzhkjoc6jfvykhegau

Use Your Brain! Arithmetic 3PC for Any Modulus with Active Security

Hendrik Eerikson, Marcel Keller, Claudio Orlandi, Pille Pullonen, Joonas Puura, Mark Simkin, Daniel Wichs, Yael Tauman Kalai, Adam D. Smith
2020 Conference on Information-Theoretic Cryptography  
This paper focuses on the specific case of actively secure three-party computation with an honest majority.  ...  Secure multiparty computation (MPC) allows a set of mutually distrustful parties to compute a public function on their private inputs without revealing anything beyond the output of the computation.  ...  We focus on a popular model of three-party computation with an honest majority.  ... 
doi:10.4230/lipics.itc.2020.5 dblp:conf/icits/EeriksonKOPP020 fatcat:fpk766fhjzfnpn7s2q2ngcsq5q

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  
In the setting of secure multiparty computation, a set of parties with private inputs wish to compute some function of their inputs without revealing anything but their output.  ...  We ran extensive experimentation comparing our different protocols with each other and with a highly-optimized implementation of semi-honest GMW.  ...  BGW3 -three rounds with an honest majority. The protocol described above that is secure with an honest majority has 4 rounds of communication.  ... 
doi:10.1145/2976749.2978347 dblp:conf/ccs/Ben-EfraimLO16 fatcat:pdeycn26lrcmnbv7erkaqnouqq

Secure and Efficient Federated Transfer Learning [article]

Shreya Sharma, Xing Chaoping, Yang Liu, Yan Kang
2019 arXiv   pre-print
This is much stronger than the semi-honest model where we assume that parties follow the protocol precisely.  ...  However, the excessive computational overhead of the security protocol involved in this model rendered it impractical.  ...  The work presented experimental results for a model based on HE and provided security in the semi-honest setting, with the major drawback being a lack of efficiency. II.  ... 
arXiv:1910.13271v2 fatcat:c2ew4au37neijncigtetclqfwy

Improved Secure Two-Party Computation via Information-Theoretic Garbled Circuits [chapter]

Vladimir Kolesnikov, Ranjit Kumaresan
2012 Lecture Notes in Computer Science  
We optimize the communication (and, indirectly, computation) complexity of two-party secure function evaluation (SFE).  ...  Motivated by the client-server setting, we propose two variants of our construction: one for semi-honest model (relatively straightforward), and one secure against a semi-honest server and covert client  ...  We also point out that the "slicing" technique is also used in [5] mainly as a method to reduce rounds in multi-party secure computation with an honest majority.  ... 
doi:10.1007/978-3-642-32928-9_12 fatcat:j35gpcti7vcwvh2z7cgechzbja

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  
As with previous works in this area aiming to achieve high efficiency, our protocol is secure with abort and does not achieve fairness, meaning that the adversary may receive output while the honest parties  ...  Our protocols are information-theoretically secure in the presence of a malicious adversaries, assuming an honest majority.  ...  This is standard in the case of no honest majority since not all functions can be computed fairly without an honest majority [9] , but security with abort can also in order to aid the construction of  ... 
doi:10.1007/978-3-319-96878-0_2 fatcat:55tehzjfi5h3ba5ot5ckt5fj7a

Secret Sharing Sharing For Highly Scalable Secure Aggregation [article]

Timothy Stevens, Joseph Near, Christian Skalka
2022 arXiv   pre-print
This ensures privacy of secret inputs in the standard real/ideal security paradigm, in both semi-honest and malicious settings where the server may collude with the adversary.  ...  Secure Multiparty Computation (MPC) can improve the security and privacy of data owners while allowing analysts to perform high quality analytics.  ...  If SHARD is implemented with semi-honest secure secret sharing, then SHARD is secure in the semi-honest model.  ... 
arXiv:2201.00864v2 fatcat:mujyxka7dbgonjzmfyffehq32y

Fast Distributed RSA Key Generation for Semi-honest and Malicious Adversaries [chapter]

Tore Kasper Frederiksen, Yehuda Lindell, Valery Osheter, Benny Pinkas
2018 Lecture Notes in Computer Science  
Finally, we implement our malicious protocol and show that its performance is an order of magnitude better than the best previous protocol, which provided only semi-honest security.  ...  We present two new, highly efficient, protocols for securely generating a distributed RSA key pair in the two-party setting. One protocol is semi-honestly secure and the other maliciously secure.  ...  Unfortunately, their protocol is only secure in the semi-honest setting, against an honest majority.  ... 
doi:10.1007/978-3-319-96881-0_12 fatcat:ki2i6hafazd53gimgmfpagmxyu

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

Rahul Rachuri, Ajith Suresh
2019 arXiv   pre-print
We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms – Linear Regression, Logistic  ...  Secure Comparison, achieve constant round complexity. The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3.  ...  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

Secure Data Exchange

Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Peter Rindal, Mike Rosulek
2019 Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop - CCSW'19  
More precisely, we describe a scenario that we call Secure Data Exchange (SDE), where several data owners are storing private encrypted data in a semi-honest non-colluding cloud, and an evaluator (a third  ...  Our main result is an efficient and practical protocol for enabling SDE using Secure Multi-Party Computation (MPC) in a novel adaptation of the server-aided setting.  ...  In a system for computations in the cloud, often there is one party with the majority of interest in the outcome of the computation.  ... 
doi:10.1145/3338466.3358924 dblp:conf/ccs/Gilad-BachrachL19 fatcat:xq3v34rn6rggfoqe334ajfelqa
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