基于同态加密的可搜索人脸识别认证

IF 3.7 2区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Baiqi Wu , Shuli Zheng , Peiming Dai , Jiazheng Chen , Yuanzhi Yao
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引用次数: 0

摘要

基于深度学习的面部识别技术的最新进展引发了人们对数据安全和隐私的严重担忧。为了最大限度地减少存储和计算开销,面部数据经常外包给云服务器进行匹配。与密码不同,面部特征可以唯一识别个人,一旦被泄露,就会产生不可逆转的风险。可搜索加密(SE)方案已经出现,以保护云中的外包数据,允许直接对加密数据进行查询。然而,现有的方法主要支持确定性精确匹配搜索,忽略了面部特征由于时间和环境因素的自然变异性,导致准确性降低。此外,对对称加密的依赖可能会损害数据的机密性和完整性。为了解决这些限制,我们提出了SFRA,一种高效且可验证的可搜索面部识别认证方案。SFRA利用位置敏感散列与双布隆过滤器相结合,生成树结构索引,存储由深度学习模型提取的加密面部特征向量。在检索过程中,使用预定义的成功匹配阈值来测量查询活板门和存储索引之间的相似性。我们还定义了一个全面的安全框架,严格证明了SFRA在三种泄漏模式下的安全性。在实际数据集上的经验实验表明,SFRA具有较好的精度和计算效率。总体而言,SFRA显著提高了云部署加密面部识别系统的安全性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Searchable face recognition authentication based on homomorphic encryption
Recent advances in deep learning-based facial recognition have sparked significant concerns over data security and privacy. To minimize storage and computational overhead, facial data is frequently outsourced to cloud servers for matching. Unlike passwords, facial features uniquely identify individuals, creating irreversible risks if compromised. Searchable Encryption (SE) schemes have emerged to protect outsourced data in the cloud, enabling queries directly over encrypted data. However, existing approaches primarily support deterministic exact match searches, neglecting the natural variability of facial features due to temporal and environmental factors, leading to decreased accuracy. Furthermore, reliance on symmetric encryption potentially compromises data confidentiality and integrity. To address these limitations, we propose SFRA, an efficient and verifiable Searchable Facial Recognition Authentication scheme. SFRA leverages locality-sensitive hashing combined with twin bloom filters to generate a tree-structured index storing encrypted facial feature vectors extracted by a deep learning model. During retrieval, the similarity between query trapdoors and stored index is measured using a predefined threshold for successful matching. We also define a comprehensive security framework and rigorously prove SFRA’s security under three leakage patterns. Empirical experiments in real-world datasets demonstrate that SFRA achieves superior accuracy and computational efficiency. Overall, SFRA significantly enhances security and efficiency in encrypted facial recognition systems for cloud deployments.
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来源期刊
Journal of Information Security and Applications
Journal of Information Security and Applications Computer Science-Computer Networks and Communications
CiteScore
10.90
自引率
5.40%
发文量
206
审稿时长
56 days
期刊介绍: Journal of Information Security and Applications (JISA) focuses on the original research and practice-driven applications with relevance to information security and applications. JISA provides a common linkage between a vibrant scientific and research community and industry professionals by offering a clear view on modern problems and challenges in information security, as well as identifying promising scientific and "best-practice" solutions. JISA issues offer a balance between original research work and innovative industrial approaches by internationally renowned information security experts and researchers.
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