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arXiv · 2309.13803

Privacy-preserving Linear Computations in Spiking Neural P Systems

Mihail-Iulian Plesa·Marian Gheorghe·Florentin Ipate
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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.

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Mihail-Iulian Plesa, Marian Gheorghe, Florentin Ipate. 2023-09-25. Privacy-preserving Linear Computations in Spiking Neural P Systems. https://doi.org/10.4204/eptcs.389.9

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