LightPHE: Integrating Partially Homomorphic Encryption into Python with Extensive Cloud Environment Evaluations

• URL: http://arxiv.org/abs/2408.05219v1
• Date: Thu, 25 Jul 2024 12:23:31 GMT
• Title: LightPHE: Integrating Partially Homomorphic Encryption into Python with Extensive Cloud Environment Evaluations
• Authors: Sefik Ilkin Serengil, Alper Ozpinar,
• Abstract summary: Homomorphic encryption enables computations on encrypted data without accessing private keys, enhancing security in cloud environments. This study introduces LightPHE, a lightweight hybrid PHE framework for Python, designed to address the lack of existing PHE libraries. LightPHE integrates multiple PHE algorithms with a modular design, ensuring robustness and usability for rapid prototyping and secure application development.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Homomorphic encryption enables computations on encrypted data without accessing private keys, enhancing security in cloud environments. Without this technology, updates need to be performed on-premises or require transmitting private keys to the cloud, increasing security risks. Fully homomorphic encryption (FHE) supports both additive and multiplicative operations on ciphertexts, while partially homomorphic encryption (PHE) supports either addition or multiplication, offering a more efficient and practical solution. This study introduces LightPHE, a lightweight hybrid PHE framework for Python, designed to address the lack of existing PHE libraries. LightPHE integrates multiple PHE algorithms with a modular and extensible design, ensuring robustness and usability for rapid prototyping and secure application development. Cloud-based experiments were conducted on Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark to evaluate LightPHE's performance and scalability. Key metrics such as key generation, encryption, decryption, and homomorphic operations were assessed. Results showed LightPHE's superior performance in high-computation environments like Colab A100 GPU and TPU2, while also offering viable options for cost-effective setups like Colab Normal and Azure Spark. Comparative analyses demonstrated LightPHE's efficiency and scalability, making it suitable for various applications. The benchmarks offer insights into selecting appropriate cloud environments based on performance needs, highlighting LightPHE's potential to advance homomorphic encryption for secure and efficient cloud-based data processing.

Related papers

• Design and Optimization of Cloud Native Homomorphic Encryption Workflows for Privacy-Preserving ML Inference [0.0]
  Homomorphic Encryption (HE) has emerged as a compelling technique that enables cryptographic computation on encrypted data.<n>The integration of HE within large scale cloud native pipelines remains constrained by high computational overhead, orchestration complexity, and model compatibility issues.<n>This paper presents a systematic framework for the design and optimization of cloud native homomorphic encryption that support privacy ML inference.
  arXiv  Detail & Related papers  (2025-10-28T15:13:32Z)
• 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)
• HHEML: Hybrid Homomorphic Encryption for Privacy-Preserving Machine Learning on Edge [4.623074730653607]
  Privacy-preserving machine learning (PPML) is an emerging topic to handle secure machine learning inference over sensitive data in untrusted environments.<n>Fully homomorphic encryption (FHE) enables computation directly on encrypted data on the server side, but it introduces significant communication and computation overhead on the client side.<n>HHE addresses this limitation by combining symmetric encryption (SE) with FHE to reduce the computational cost on the client side.<n>Our work proposes a hardware-accelerated HHE architecture built around a lightweight symmetric cipher optimized for FHE compatibility.
  arXiv  Detail & Related papers  (2025-10-23T05:51:55Z)
• CryptOracle: A Modular Framework to Characterize Fully Homomorphic Encryption [3.5348336893819554]
  This paper presents a detailed characterization of OpenFHE, a comprehensive open-source library for FHE.<n>We introduce CryptOracle, a modular evaluation framework comprising (1) a benchmark suite, (2) a hardware profiler, and (3) a predictive performance model.
  arXiv  Detail & Related papers  (2025-10-03T23:30:29Z)
• Efficient Multi-Instance Generation with Janus-Pro-Dirven Prompt Parsing [53.295515505026096]
  Janus-Pro-driven Prompt Parsing is a prompt- parsing module that bridges text understanding and layout generation. MIGLoRA is a parameter-efficient plug-in integrating Low-Rank Adaptation into UNet (SD1.5) and DiT (SD3) backbones. The proposed method achieves state-of-the-art performance on COCO and LVIS benchmarks while maintaining parameter efficiency.
  arXiv  Detail & Related papers  (2025-03-27T00:59:14Z)
• Cross-Platform Benchmarking of the FHE Libraries: Novel Insights into SEAL and Openfhe [0.5991851254194097]
  Homomorphic encryption (HE) has become a vital solution for addressing privacy concerns. This paper provides a comprehensive evaluation of two leading HE libraries, SEAL and OpenFHE.
  arXiv  Detail & Related papers  (2025-03-14T09:08:30Z)
• Hades: Homomorphic Augmented Decryption for Efficient Symbol-comparison -- A Database's Perspective [1.3824176915623292]
  This paper introduces HADES, a novel cryptographic framework that enables efficient and secure comparisons on encrypted data. Based on the Ring Learning with Errors (RLWE) problem, HADES provides CPA-security and incorporates perturbation-aware encryption to mitigate frequency-analysis attacks.
  arXiv  Detail & Related papers  (2024-12-28T02:47:14Z)
• At Least Factor-of-Two Optimization for RWLE-Based Homomorphic Encryption [0.0]
  Homomorphic encryption (HE) supports certain operations on encrypted data without the need for decryption. HE schemes come with a non-trivial computational overhead that can hamper data-intensive workloads. We present an encryption method we call Zinc" which forgoes the multiple caching process, replacing it with a single scalar addition.
  arXiv  Detail & Related papers  (2024-08-14T05:42:35Z)
• Enc2DB: A Hybrid and Adaptive Encrypted Query Processing Framework [47.11111145443189]
  We introduce Enc2DB, a novel secure database system following a hybrid strategy on and openGauss. We present a micro-benchmarking test and self-adaptive mode switch strategy that can choose the best execution path (cryptography or TEE) to answer a given query. We also design and implement a ciphertext index compatible with native cost model and querys to accelerate query processing.
  arXiv  Detail & Related papers  (2024-04-10T08:11:12Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• 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)
• EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing [9.41257807502252]
  We propose a novel paradigm named Encrypted Vision Transformer (EViT), which advances the discriminative representations capability of cipher-images. EViT achieves both excellent encryption and retrieval performance, outperforming current schemes in terms of retrieval accuracy by large margins while protecting image privacy effectively.
  arXiv  Detail & Related papers  (2022-08-31T07:07:21Z)
• 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)
• Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm [140.25480610981504]
  A complete list of metrics to evaluate VFL algorithms should include model applicability, privacy, communication, and computation efficiency. We propose a novel VFL framework with black-box scalability, which is inseparably inseparably scalable.
  arXiv  Detail & Related papers  (2022-03-19T13:55:47Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution [2.9692754277987286]
  Cloud-based machine learning services (CMLS) enable organizations to take advantage of advanced models that are pre-trained on large quantities of data. Asymmetric encryption requires the data to be decrypted in the cloud, while Homomorphic encryption is often too slow and difficult to implement. We propose One Way Scrambling by Deconvolution (OWSD), a deconvolution-based scrambling framework that offers the advantages of Homomorphic encryption at a fraction of the computational overhead.
  arXiv  Detail & Related papers  (2021-11-04T19:46:41Z)
• Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities [19.532232041651522]
  We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. operating on the encrypted data on the server side is equivalent to operating on the original data.
  arXiv  Detail & Related papers  (2020-12-19T14:20:56Z)

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.