Recently, massive multimedia data (especially images) is moved to the cloud environment for analysis and retrieval, which makes data security issue become particularly significant. Image similarity join has attracted more and more attention in the community of multimedia retrieval. However, few researches have investigated the privacy-preserving problem of image similarity join. To tackle this challenge, this paper proposes a novel privacy-preserving image similarity join method, called PPIS-JOIN. Different from the existing schemes, this approach aims to combine deep image hashing method and a novel affine transformation method to conceal sensitive information at feature level and generate high quality hash codes. Meanwhile, based on secure hash codes, a privacy-preserving similarity query model is proposed, which includes a secure image hash codes based inverted index, called ISH-Index, to support efficient and accuracy similarity search. We conduct comprehensive experiments on three common used benchmarks, and the results demonstrate the performance of the proposed PPIS-JOIN outperforms baselines.

近年来，海量多媒体数据（尤其是图像）迁移到云环境进行分析检索，使得数据安全问题尤为突出。图像相似度连接在多媒体检索界越来越受到关注。然而，很少有研究调查图像相似性连接的隐私保护问题。为了应对这一挑战，本文提出了一种新颖的隐私保护图像相似性连接方法，称为 PPIS-JOIN。与现有方案不同，该方法旨在结合深度图像哈希方法和新颖的仿射变换方法，在特征级别隐藏敏感信息并生成高质量的哈希码。同时，基于安全哈希码，提出了一种隐私保护的相似性查询模型，其中包括一个基于安全图像哈希码的倒排索引，称为 ISH-Index，以支持高效和准确的相似性搜索。我们对三个常用的基准进行了全面的实验，结果证明了所提出的 PPIS-JOIN 的性能优于基线。