Work

The Least Information Set Needed by Privacy Attackers

Year: 2024

Type: article

Abstract: This article studies privacy-preserving problems focusing on the information that is available to the attacker. For a class of general linear average consensus algorithms, we identify the least information set required by an attacker to infer the initial value of the target node. We then propose a novel algorithm that protects the initial value effectively if the attacker fails to have this least information. Furthermore, we show that the initial value of nodes may be disclosed even if the attacker does not know any information exchanged between the target node and its neighbors. Finally, we propose a novel eavesdropping algorithm if the least information is available. Several numerical examples are given to verify the validity of our results.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2024.3470848

Authors: Antai Xie, Xiaofan Wang, Wen Yang, Ming Cao, Xiaoqiang Ren

Institutions: Shanghai University, East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, University of Groningen

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G1642170986, G4405644799, G6327818892