Homomorphic encryption schemes based on coding theory and polynomials
- URL: http://arxiv.org/abs/2509.13788v1
- Date: Wed, 17 Sep 2025 07:58:18 GMT
- Title: Homomorphic encryption schemes based on coding theory and polynomials
- Authors: Giovanni Giuseppe Grimaldi,
- Abstract summary: Homomorphic encryption is a powerful cryptographic tool that enables secure computations on the private data.<n>This is crucial for any sensitive application running in the Cloud, because we must protect data privacy even in the case when the server has falled victim to a cyber attack.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Homomorphic encryption is a powerful cryptographic tool that enables secure computations on the private data. It evaluates any function for any operation securely on the encrypted data without knowing its corresponding plaintext. For original data $p$, $c$ denotes the ciphertext of the original plaintext $p$, i.e. $c = Encrypt_k(p)$. This is crucial for any sensitive application running in the Cloud, because we must protect data privacy even in the case when the server has falled victim to a cyber attack. The encryption scheme $Encrypt_k$ is said to be homomorphic with respect to some set of operations $\mathcal{O}$, if for any operation $\circ \in \mathcal{O}$ one can compute $Encrypt_k(p_1 \circ p_2)$ from $Encrypt_k(p_1) \circ Encrypt_k(p_2)$. Those schemes come in three forms: somewhat, partially and fully homomorphic. In this survey, we present the state of art of the known homomorphic encryption schemes based on coding theory and polynomials.
Related papers
- DictPFL: Efficient and Private Federated Learning on Encrypted Gradients [46.7448838842482]
We present DictPFL, a framework that achieves full gradient protection with minimal overhead.<n>It encrypts every transmitted gradient while keeping non-transmitted parameters local, preserving privacy without heavy computation.<n>Experiments show that DictPFL reduces communication cost by 402-748$times$ and accelerates training by 28-65$times$ compared to fully encrypted FL.
arXiv Detail & Related papers (2025-10-24T01:58:42Z) - Anamorphic Cryptography using Baby-Step Giant-Step Recovery [0.46040036610482665]
This paper outlines the implementation of Anamorphic Cryptography using ECC (Elliptic Curve Cryptography)<n>It outlines how the secret message sent to Alice is hidden within the random nonce value, which is used within the encryption process.<n>It also shows that the BSGS (Baby-step Giant-step) variant significantly outperforms unoptimised elliptic curve methods.
arXiv Detail & Related papers (2025-04-21T12:53:32Z) - Cryptanalysis on Lightweight Verifiable Homomorphic Encryption [7.059472280274008]
Verifiable Homomorphic Encryption (VHE) is a cryptographic technique that integrates Homomorphic Encryption (HE) with Verifiable Computation (VC)<n>It serves as a crucial technology for ensuring both privacy and integrity in outsourced computation.<n>This paper presents efficient attacks that exploit the homomorphic properties of encryption schemes.
arXiv Detail & Related papers (2025-02-18T08:13:10Z) - Optimal Computational Secret Sharing [51.599517747577266]
In $(t, n)$-threshold secret sharing, a secret $S$ is distributed among $n$ participants.<n>We present a construction achieving a share size of $tfrac|S|t + |K|t$.
arXiv Detail & Related papers (2025-02-04T23:37:16Z) - The Evolution of Cryptography through Number Theory [55.2480439325792]
cryptography began around 100 years ago, its roots trace back to ancient civilizations like Mesopotamia and Egypt.<n>This paper explores the link between early information hiding techniques and modern cryptographic algorithms like RSA.
arXiv Detail & Related papers (2024-11-11T16:27:57Z) - An Attack on $p$-adic Lattice Public-key Cryptosystems and Signature Schemes [3.444630356331766]
In this paper, we improve the LVP algorithm in local fields.<n>We utilize this algorithm to attack the above schemes so that we are able to forge any message and decrypt any ciphertext.<n>Although these schemes are broken, this work does not mean that $p$-adic lattices are not suitable in constructing cryptographic primitives.
arXiv Detail & Related papers (2024-09-13T12:31:57Z) - Conditional Encryption with Applications to Secure Personalized Password Typo Correction [7.443139252028032]
We introduce the notion of a conditional encryption scheme as an extension of public key encryption.
A conditional encryption scheme for a binary predicate $P$ adds a new conditional encryption algorithm $mathsfCEnc$.
We demonstrate how to use conditional encryption to improve the security of personalized password typo correction systems.
arXiv Detail & Related papers (2024-09-10T00:49:40Z) - Publicly-Verifiable Deletion via Target-Collapsing Functions [81.13800728941818]
We show that targetcollapsing enables publiclyverifiable deletion (PVD)
We build on this framework to obtain a variety of primitives supporting publiclyverifiable deletion from weak cryptographic assumptions.
arXiv Detail & Related papers (2023-03-15T15:00:20Z) - RiDDLE: Reversible and Diversified De-identification with Latent
Encryptor [57.66174700276893]
This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor.
Built upon a pre-learned StyleGAN2 generator, RiDDLE manages to encrypt and decrypt the facial identity within the latent space.
arXiv Detail & Related papers (2023-03-09T11:03:52Z) - THE-X: Privacy-Preserving Transformer Inference with Homomorphic
Encryption [112.02441503951297]
Privacy-preserving inference of transformer models is on the demand of cloud service users.
We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
arXiv Detail & Related papers (2022-06-01T03:49:18Z) - A brief history on Homomorphic learning: A privacy-focused approach to
machine learning [2.055949720959582]
Homomorphic encryption allows running arbitrary operations on encrypted data.
It enables us to run any sophisticated machine learning algorithm without access to the underlying raw data.
It took more than 30 years of collective effort to finally find the answer "yes"
arXiv Detail & Related papers (2020-09-09T21:57:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.