Privacy-preserving Linear Computations in Spiking Neural P Systems

TL;DR

This paper introduces a privacy-preserving protocol for clients to compute linear functions using Spiking Neural P systems on remote servers, ensuring data privacy and security in computational processes.

Contribution

It presents a novel protocol enabling secure linear computations with SN P systems, addressing privacy concerns in remote biological-inspired computing models.

Findings

Protocol securely computes linear functions without revealing inputs.

SN P system implementation supports any linear function over natural numbers.

Security analysis confirms protocol's robustness in honest-but-curious model.

Abstract

Spiking Neural P systems are a class of membrane computing models inspired directly by biological neurons. Besides the theoretical progress made in this new computational model, there are also numerous applications of P systems in fields like formal verification, artificial intelligence, or cryptography. Motivated by all the use cases of SN P systems, in this paper, we present a new privacy-preserving protocol that enables a client to compute a linear function using an SN P system hosted on a remote server. Our protocol allows the client to use the server to evaluate functions of the form t_1k + t_2 without revealing t_1, t_2 or k and without the server knowing the result. We also present an SN P system to implement any linear function over natural numbers and some security considerations of our protocol in the honest-but-curious security model.