Dimension Reduction via Random Projection for Privacy in Multi-Agent Systems

Autor:	Ghoshal, Puspanjali, Sairam, Ashok Singh
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	The agents in a Multi-Agent System (MAS) make observations about the system and send that information to a fusion center. The fusion center aggregates the information and concludes about the system parameters with as much accuracy as possible. However for the purposes of better efficiency of the system at large, the agents need to append some private parameters to the observed data. In this scenario, the data sent to the fusion center is faced with privacy risks. The data communicated to the fusion center must be secured against data privacy breaches and inference attacks in a decentralized manner. However, this in turn leads to a loss of utility of the data being sent to the fusion center. We quantify the utility and privacy of the system using Cosine similarity. We formulate our MAS problem in terms of deducing a concept for which compression-based methods are there in literature. Next, we propose a novel sanitization mechanism for our MAS using one such compression-based method while addressing the utility-privacy tradeoff problem. Comment: This work has been submitted to the IEEE for possible publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.04031 Zobrazit plný text záznamu View this record from Arxiv