Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines
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Abstract (arXiv)
We propose protocols for obliviously evaluating finite-state machines, i.e., the evaluation is shared between the provider of the finite-state machine and the provider of the input string in such a manner that neither party learns the other's input, and the states being visited are hidden from both. For alphabet size |Sigma|, number of states |Q|, and input length n, previous solutions have either required a number of rounds linear in n or communication Omega(n|Sigma||Q|log|Q|). Our solutions require 2 rounds with communication O(n(|Sigma|+|Q|log|Q|)). We present two different solutions to this problem, a two-party one and a setting with an untrusted but non-colluding helper.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 37 references that are in the map. First posted on arXiv 20 Mar 2021.
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References · 12
- 2020CrypTFlow2: Practical 2-Party Secure Inferencecited by 82
- 2019CrypTFlow: Secure TensorFlow Inferencecited by 61
- 2019XONN: XNOR-based Oblivious Deep Neural Network Inferencecited by 51
- 2019QUOTIENT: Two-Party Secure Neural Network Training and Predictioncited by 41
- 2019Secure Evaluation of Quantized Neural Networkscited by 22
- 2019Protecting Privacy of Users in Brain-Computer Interface Applicationscited by 13
- 2021Privacy-Preserving Feature Selection with Secure Multiparty Computationcited by 12
- 2021Privacy-Preserving Video Classification with Convolutional Neural Networkscited by 10
- 2020High Performance Logistic Regression for Privacy-Preserving Genome Analysiscited by 9
- 2019Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detectioncited by 5
- 2021Fast Privacy-Preserving Text Classification based on Secure Multiparty Computationcited by 2
- 2020Private Speech Classification with Secure Multiparty Computationcited by 1
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