Privacy-Preserving Distributed Nonnegative Matrix Factorization
Ehsan Lari Reza Arablouei Stefan Werner
Nonnegative matrix factorization (NMF) is an effective data representation
tool with numerous applications in signal processing and machine learning.
However, deploying NMF in a decentralized manner over ad-hoc networks
introduces privacy concerns due to the conventional approach of sharing raw
data among network agents. To address this, we propose a privacy-preserving
algorithm for fully-distributed NMF that decomposes a distributed large data
matrix into left and right matrix factors while safeguarding each agent's local
data privacy. It facilitates collaborative estimation of the left matrix factor
among agents and enables them to estimate their respective right factors
without exposing raw data. To ensure data privacy, we secure information
exchanges between neighboring agents utilizing the Paillier cryptosystem, a
probabilistic asymmetric algorithm for public-key cryptography that allows
computations on encrypted data without decryption. Simulation results conducted
on synthetic and real-world datasets demonstrate the effectiveness of the
proposed algorithm in achieving privacy-preserving distributed NMF over ad-hoc
networks.