A Privacy-Preserving Service Framework for Traveling Salesman Problem-Based Neural Combinatorial Optimization Network

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neura...

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Bibliographic Details
Published in:IEEE transactions on cloud computing Vol. 11; no. 4; pp. 1 - 14
Main Authors: Liu, Jiao, Li, Xinghua, Liu, Ximeng, Tang, Jiawei, Ma, Siqi, Weng, Jian
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Cloud computing
Combinatorial analysis
Computational modeling
Data processing
Decoding
Neural network
Neural networks
Optimization
Outsourcing
Privacy
privacy-preserving service
Protocols
secret sharing
secure multi-party computation
Servers
Traveling salesman problem
TSP
ISSN:2168-7161, 2372-0018
Online Access:Get full text
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Summary:The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's data are directly outsourced to a cloud platform to provide and request service. In order to address the issue, this paper proposes a privacy-preserving service framework for a TSP-based neural combinatorial optimization network called PPSF. We first protect the service provider's model parameters and users' data by different split methods in multiple cloud servers, providing a secure outsourced mode for the participators in the PPSF framework. Then, in the secure outsourced mode, a series of secure computation protocols are designed for the cloud servers to support performing the secure computing in each service task. Moreover, it can also protect the process that the cloud servers respond to users after data processing and achieve private service result recovery. Finally, we prove that the proposed framework can realize privacy protection for TSP-based combinatorial optimization service and verify its utility and efficiency by experiments.
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ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2023.3287552