Privacy-Preserving Distributed Nonnegative Matrix Factorization
This addresses privacy issues for applications in signal processing and machine learning where distributed NMF is needed, but it appears incremental as it adapts existing cryptographic methods to a known bottleneck.
The paper tackled the problem of privacy concerns in deploying nonnegative matrix factorization (NMF) over decentralized ad-hoc networks by proposing a privacy-preserving algorithm that uses the Paillier cryptosystem to secure data exchanges, with simulation results demonstrating its effectiveness on synthetic and real-world datasets.
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.