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Article

A privacy-preserving protocol for neural-network-based computation

Authors:

A. PivaAuthors Info & Claims

MM&Sec '06: Proceedings of the 8th workshop on Multimedia and security

Pages 146 - 151

https://doi.org/10.1145/1161366.1161393

Published: 26 September 2006 Publication History

Abstract

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. Two different levels of protection are considered: according to the first one only the NN weights are protected, whereas the second level also permits to protect the node activation functions. An efficient way of implementing the proposed protocol by means of some recently proposed multi-party computation techniques is described.

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Cited By

Choe HLuo BLiao XXu JKirda ELie D(2024)Toward Practical Threshold FHE: Low Communication, Computation and InteractionProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690861(5107-5109)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690861
Liu LFu SHuang XLuo YZhang XChoo K(2024)SecDM: A Secure and Lossless Human Mobility Prediction SystemIEEE Transactions on Services Computing10.1109/TSC.2024.335829217:4(1793-1805)Online publication date: Jul-2024
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Yang XChen JLi YHe KHuang XJiang ZBai HDu R(2024)Fregata: Fast Private Inference With Unified Secure Two-Party ProtocolsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344432719(8472-8484)Online publication date: 2024
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Index Terms

A privacy-preserving protocol for neural-network-based computation
1. Security and privacy
  1. Human and societal aspects of security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Intellectual property
    2. Privacy policies

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Information & Contributors

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Published In

cover image ACM Conferences

MM&Sec '06: Proceedings of the 8th workshop on Multimedia and security

September 2006

244 pages

ISBN:1595934936

DOI:10.1145/1161366

General Chair:
Slava Voloshynovskiy
University of Geneva, Switzerland
,
Program Chairs:
Jana Dittmann
Otto-von-Guericke University, Magdeburg, Germany
,
Jessica Fridrich
SUNY Binghamton, USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 26 September 2006

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MM&Sec '06

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MM&Sec '06: Multimedia and Security Workshop

September 26 - 27, 2006

Geneva, Switzerland

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Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Choe HLuo BLiao XXu JKirda ELie D(2024)Toward Practical Threshold FHE: Low Communication, Computation and InteractionProceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security10.1145/3658644.3690861(5107-5109)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3658644.3690861
Liu LFu SHuang XLuo YZhang XChoo K(2024)SecDM: A Secure and Lossless Human Mobility Prediction SystemIEEE Transactions on Services Computing10.1109/TSC.2024.335829217:4(1793-1805)Online publication date: Jul-2024
https://doi.org/10.1109/TSC.2024.3358292
Yang XChen JLi YHe KHuang XJiang ZBai HDu R(2024)Fregata: Fast Private Inference With Unified Secure Two-Party ProtocolsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.344432719(8472-8484)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3444327
Merkle FSamsinger MSchöttle PPevny T(2024)On the Economics of Adversarial Machine LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.337982919(4670-4685)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3379829
Gong MZhang YGao YQin AWu YWang SZhang Y(2024)A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic EncryptionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.334099419(1826-1839)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3340994
Kumar AChoi B(2024)Global Model Privacy Protection Mechanism in Federated Learning2024 International Conference on Information Networking (ICOIN)10.1109/ICOIN59985.2024.10572139(398-402)Online publication date: 17-Jan-2024
https://doi.org/10.1109/ICOIN59985.2024.10572139
Liu QHuang QChen XWang SWang WHan SLee P(2024)PP-Stream: Toward High-Performance Privacy-Preserving Neural Network Inference via Distributed Stream Processing2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00123(1492-1505)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00123
Moghaddam AGanesh BPalmieri P(2024)Privacy-Preserving Sentiment Analysis Using Homomorphic Encryption and Attention MechanismsApplied Cryptography and Network Security Workshops10.1007/978-3-031-61489-7_6(84-100)Online publication date: 29-Jun-2024
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Nikfam FCasaburi RMarchisio AMartina MShafique M(2023)A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural NetworksInformation10.3390/info1410053714:10(537)Online publication date: 1-Oct-2023
https://doi.org/10.3390/info14100537
Kitai HYanai NIwahana KTatsumi MCruz J(2023)A Study on Quantized Parameters for Protection of a Model and Its Inference InputJournal of Information Processing10.2197/ipsjjip.31.66731(667-678)Online publication date: 2023
https://doi.org/10.2197/ipsjjip.31.667
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