Privacy-preserving Linear Computations in Spiking Neural P Systems

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.Comment: In Proceedings FROM 2023, arXiv:2309.1295

A New Quantum Encryption Scheme

The model of quantum computation has advanced very quickly in the last years. This model brings with it an efficient algorithm for factoring, namely the Shor algorithm. This means that the public key infrastructure will soon be obsolete. In this paper we propose a new quantum cryptographic scheme which aims to replace the RSA algorithm from current public key infrastructures. We analyze the security of our scheme and also, we describe the implementation of the scheme using IBM Q SDK, qiskit. We run a number of experiments in order to build a proof of concept application that uses the proposed scheme