TASTY: tool for automating secure two-party computations

By Wilko Henecka, Stefan Kögl, Ahmad-Reza Sadeghi, Thomas Schneider, Immo Wehrenberg. 2010

411 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, methodology): “Popular applications used for the empirical evaluation of practical MPC systems include graph algorithms [5, 32, 42, 51, 57], data structure algorithms [42, 51], string matching and distance algorithms [36, 37, 66] and AES [3, 36, 37, 66].”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, background): “TASTY [28] and more recently L1 [29] extend Fairplay by reverting to homomorphic encryption when the latter is more efficient.”
Gazelle: A Low Latency Framework for Secure Neural Network Inference (2018, neutral): “We note that this conversion strategy is broadly similar to the one developed in [25].”
Secure Multiparty Computation from SGX (2017, background): “The trust barrier between parties is overcome using cryptographic techniques that permit computing over encrypted and/or secret-shared data [31,25,18].”
Server-Aided Secure Computation with Off-line Parties (2017, methodology): “We design two MPC-based constructions based on well-studied techniques for secure two-party computation, garbled circuits [49,50] and mixed protocols [13,21,28,34].”
Secure Linear Regression on Vertically Partitioned Datasets (2016, methodology): “Popular applications used for the empirical evaluation of implemented systems include graph algorithms [6, 18, 24, 29, 34, 45], data structure algorithms [24,29,42], string matching and distance algorithms [20,21,39] and AES [4,20,21,39].”
Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations (2015, positive): “A similar approach is also used in the TASTY framework that combines GC and additively homomorphic encryption in a two-party setting [16].”
Compacting privacy-preserving k-nearest neighbor search using logic synthesis (2015, methodology): “Following the introduction of the first compiler for GC, called Fairplay [23], a number of researchers have focused on providing a custom compiler to interpret high level procedural language and map it to a circuit description language [9, 11].”
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution (2012, methodology): “Most implementations of generic secure two-party computation have targeted the semi-honest threat model [29, 14, 16], and have used protocols based on Yao’s garbled-circuit approach.”
Machine Learning Classification over Encrypted Data (2015, methodology): “A set of generic secure two- or multi-party computation tools have been developed, such as TASTY [6] and Fairplay [7], [8].”
Private Set Intersection: Are Garbled Circuits Better than Custom Protocols? (2012, background): “TASTY [16] extended Fairplay to give programmers the flexibility of switching between garbled circuits and approaches using homomorphic encryption.”
Billion-Gate Secure Computation with Malicious Adversaries (2012, result): “Our results are in the standard model (i.e., no common reference strings or PKIs) and, in contrast to prior work, we do not use the random oracle model which has well-established theoretical shortcomings.”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Some provide secure integer arithmetic [20], [21], [22], [23], [24], [25], [26], floating point arithmetic [27], [28], shuffling and sorting [29] and linking [4].”
Secure two-party computations in ANSI C (2012, background): “Copyright 2012 ACM 978-1-4503-1651-4/12/10 ...$15.00.”
Efficient Secure Computation with Garbled Circuits (2011, background): “TASTY [26] extended Fairplay’s SFDL to allow the programmer to specify where in the digital circuit to integrate some arithmetic circuits (limited to addition and constant multiplication) that are realized by additive homomorphic encryption schemes.”
Deploying Secure Multi-Party Computation for Financial Data Analysis - (Short Paper) (2012, background): “MPC has been studied for almost thirty years and recently, many MPC projects have started reaching practical results [9,10,1,7,13,15,4,11].”
More efficient oblivious transfer and extensions for faster secure computation (2013, neutral): “Accordingly, the eﬃcient instantiation of OT is of crucial importance as is evident in many recent works that focus on eﬃciency, e.g., [8,16,19,22–24, 26, 34, 37, 43, 49 ].”