arXiv subjects

Alper Ozpinar

Publications and source records attributed to Alper Ozpinar.

5 recordsLinked to original sources

arXiv2026

Bridging the Gap Between PHE and FHE: A Performance and Trade-off Analysis of The Somewhat Homomorphic BGN Cryptosystem

Homomorphic encryption (HE) enables privacy-preserving data analytics, but practitioners often face a trade-off between lightweight Partially Homomorphic Encryption (PHE) and computationally dominant Fully Homomorphic Encryption (FHE). The Boneh-Goh-Nissim (BGN) cryptosystem bridges this gap as a Somewhat Homomorphic Encryption (SWHE) scheme supporting unlimited additions and one ciphertext multiplication. Despite its algebraic elegance, practical BGN adoption has been hindered by a lack of accessible software implementations. This paper presents a comparative analysis of BGN against PHE and FHE paradigms through its integration into the lightphe Python framework, allowing deployment in just a few lines of code. We benchmark encrypted 128-dimensional vector operations under 80-bit, 112-bit and 128-bit security levels against Paillier, Damgard-Jurik, Okamoto-Uchiyama, and the FHE CKKS scheme via TenSEAL. Results reveal a computation-communication trade-off: BGN is computationally slower due to bilinear pairings compared to PHE and SIMD-optimized FHE, but retains a microscopic public key size of 3-6 KB, up to five orders of magnitude smaller than FHE. Crucially, BGN enables boundless homomorphic aggregation after a single multiplication, supporting complex tasks such as linear regression inference, Cosine Similarity, and Squared Euclidean Distance. Furthermore, an optimized precision of 2 digits suffices to match plaintext ranking baselines, overcoming the target-group discrete logarithm decryption bottleneck. By open-sourcing this pipeline in lightphe, this work establishes BGN as a practical engine for bandwidth-constrained, decentralized architectures.

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arXiv2025

LightDSA: A Python-Based Hybrid Digital Signature Library and Performance Analysis of RSA, DSA, ECDSA and EdDSA in Variable Configurations, Elliptic Curve Forms and Curves

Digital signature algorithms (DSAs) are fundamental to cryptographic security, ensuring data integrity and authentication. While RSA, DSA, ECDSA, and EdDSA are widely used, their performance varies significantly depending on key sizes, hash functions, and elliptic curve configurations. In this paper, we introduce LightDSA, a hybrid and configurable digital signature library that supports RSA, DSA, ECDSA, and EdDSA with flexible form and curve selection, open sourced at https://github.com/serengil/LightDSA. Unlike conventional implementations that impose strict curve-form mappings - such as Weierstrass for ECDSA and Edwards for EdDSA LightDSA - allows arbitrary combinations, enabling a broader performance evaluation. We analyze the computational efficiency of these algorithms across various configurations, comparing key generation, signing, and verification times. Our results provide insights into the trade-offs between security and efficiency, guiding the selection of optimal configurations for different cryptographic needs.

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arXiv2025

Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis

This paper explores the use of partially homomorphic encryption (PHE) for encrypted vector similarity search, with a focus on facial recognition and broader applications like reverse image search, recommendation engines, and large language models (LLMs). While fully homomorphic encryption (FHE) exists, we demonstrate that encrypted cosine similarity can be computed using PHE, offering a more practical alternative. Since PHE does not directly support cosine similarity, we propose a method that normalizes vectors in advance, enabling dot product calculations as a proxy. We also apply min-max normalization to handle negative dimension values. Experiments on the Labeled Faces in the Wild (LFW) dataset use DeepFace's FaceNet128d, FaceNet512d, and VGG-Face (4096d) models in a two-tower setup. Pre-encrypted embeddings are stored in one tower, while an edge device captures images, computes embeddings, and performs encrypted-plaintext dot products via additively homomorphic encryption. We implement this with LightPHE, evaluating Paillier, Damgard-Jurik, and Okamoto-Uchiyama schemes, excluding others due to performance or decryption complexity. Tests at 80-bit and 112-bit security (NIST-secure until 2030) compare PHE against FHE (via TenSEAL), analyzing encryption, decryption, operation time, cosine similarity loss, key/ciphertext sizes. Results show PHE is less computationally intensive, faster, and produces smaller ciphertexts/keys, making it well-suited for memory-constrained environments and real-world privacy-preserving encrypted similarity search.

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arXiv2025

CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition

Facial recognition systems rely on embeddings to represent facial images and determine identity by verifying if the distance between embeddings is below a pre-tuned threshold. While embeddings are not reversible to original images, they still contain sensitive information, making their security critical. Traditional encryption methods like AES are limited in securely utilizing cloud computational power for distance calculations. Homomorphic Encryption, allowing calculations on encrypted data, offers a robust alternative. This paper introduces CipherFace, a homomorphic encryption-driven framework for secure cloud-based facial recognition, which we have open-sourced at http://github.com/serengil/cipherface. By leveraging FHE, CipherFace ensures the privacy of embeddings while utilizing the cloud for efficient distance computation. Furthermore, we propose a novel encrypted distance computation method for both Euclidean and Cosine distances, addressing key challenges in performing secure similarity calculations on encrypted data. We also conducted experiments with different facial recognition models, various embedding sizes, and cryptosystem configurations, demonstrating the scalability and effectiveness of CipherFace in real-world applications.

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arXiv2024

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

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.

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