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

• URL: http://arxiv.org/abs/2502.18514v1
• Date: Sat, 22 Feb 2025 19:03:04 GMT
• Title: CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition
• Authors: Sefik Serengil, Alper Ozpinar,
• Abstract summary: This paper introduces CipherFace, a homomorphic encryption-driven framework for secure cloud-based facial recognition.<n>By leveraging FHE, CipherFace ensures the privacy of embeddings while utilizing the cloud for efficient distance computation.<n>We propose a novel encrypted distance computation method for both Euclidean and Cosine, addressing key challenges in performing secure similarity calculations on encrypted data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Encrypted Vector Similarity Computations Using Partially Homomorphic Encryption: Applications and Performance Analysis [0.0]
  We show encrypted cosine similarity can be computed using partially homomorphic encryption (PHE) PHE is less computationally intensive, faster, and produces smaller ciphertexts/keys. Results show PHE is well-suited for memory-constrained environments and real-world privacy-preserving encrypted similarity search.
  arXiv  Detail & Related papers  (2025-03-07T09:52:16Z)
• Secure Semantic Communication With Homomorphic Encryption [52.5344514499035]
  This paper explores the feasibility of applying homomorphic encryption to SemCom.<n>We propose a task-oriented SemCom scheme secured through homomorphic encryption.
  arXiv  Detail & Related papers  (2025-01-17T13:26:14Z)
• Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data [0.873811641236639]
  Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached.<n>This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues.<n>The proposed system creates a distinct 32-bit encryption key derived from facial features.
  arXiv  Detail & Related papers  (2024-12-09T19:12:17Z)
• Enabling Practical and Privacy-Preserving Image Processing [5.526464269029825]
  Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. Traditional FHE methods often encrypt images by monolithic data blocks, instead of pixels. We propose and implement a pixel-level homomorphic encryption approach, iCHEETAH, based on the CKKS scheme.
  arXiv  Detail & Related papers  (2024-09-05T14:22:02Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow [69.78820726573935]
  We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
  arXiv  Detail & Related papers  (2023-07-18T10:55:54Z)
• 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)
• Verifiable Encodings for Secure Homomorphic Analytics [10.402772462535884]
  Homomorphic encryption is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations. We implement our solution in VERITAS, a ready-to-use system for verification of outsourced computations executed over encrypted data.
  arXiv  Detail & Related papers  (2022-07-28T13:22:21Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• Speckle-based optical cryptosystem and its application for human face recognition via deep learning [17.169570487230747]
  Face images are sensitive biometric data that should be carefully protected. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption.
  arXiv  Detail & Related papers  (2022-01-26T07:18:02Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

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.