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**2024/1760**

**Title:**

Somewhat Homomorphic Encryption from Linear Homomorphism and Sparse LPN

**Authors:**
Henry Corrigan-Gibbs, Alexandra Henzinger, Yael Kalai, Vinod Vaikuntanathan

**Abstract:**

We construct somewhat homomorphic encryption schemes from the learning sparse parities with noise (sparse LPN) problem, along with an assumption that implies linearly homomorphic encryption (e.g., the decisional Diffie-Hellman or decisional composite residuosity assumptions). Our resulting schemes support an a-priori bounded number of homomorphic operations: O(\log \lambda/\log \log \lambda) multiplications followed by poly(\lambda) additions, where \lambda \in \mathbb{N} is a security parameter. These schemes have compact ciphertexts: after homomorphic evaluation, the bit-length of each ciphertext is a fixed polynomial in the security parameter \lambda, independent of the number of homomorphic operations applied to it. This gives the first somewhat homomorphic encryption schemes that can evaluate the class of bounded-degree polynomials with a bounded number of monomials without relying on lattice assumptions or bilinear maps.

Much like in the Gentry-Sahai-Waters fully homomorphic encryption scheme, ciphertexts in our scheme are matrices, homomorphic addition is matrix addition, and homomorphic multiplication is matrix multiplication. Moreover, when encrypting many messages at once and performing many homomorphic evaluations at once, the bit-length of ciphertexts in some of our schemes (before and after homomorphic evaluation) can be arbitrarily close to the bit-length of the plaintexts. The main limitation of our schemes is that they require a large evaluation key, whose size scales with the complexity of the homomorphic computation performed, though this key can be re-used across any polynomial number of encryptions and evaluations.

**ePrint:**
https://eprint.iacr.org/2024/1760

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