Research on Cloud Computing Security & Privacy Protection of Massive Unstructured Multi-Modal Data

Hui Yang, Yang Cao
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Abstract

With the rapid development of big data and AI technologies, the sharing and opening-up, development and utilization of big data from government, medical becomes into one of our priorities. However, a problem that has puzzled us for a long time is that, the usage rate of massive unstructured multi-modal data as an absolute main body is still very low, which seriously restricts the circulation of data factors and faces the increasingly serious challenge of security-compliance. To speed up the circulation process and value release of data factors, this paper proposed a security-compliant and lightweight cloud computing model for massive unstructured multi-modal data. The implementation method includes multi-modal feature recognition and extraction from unstructured big data with NLP, CNN, image processing, audio processing technologies, fast and safe cipher-text calculation, and cipher-text retrieval with HE (homomorphic encryption) against the feature index database, and multi-modal data fusion, etc. With the above model & algorithms including the optimization solution of the HE algorithm, we verified the “compute-able but invisible” of massive cross-media unstructured data, and proved the security, validity, and time efficiency.
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海量非结构化多模态数据的云计算安全与隐私保护研究
随着大数据和人工智能技术的快速发展,政府、医疗大数据的共享、开放、开发和利用成为我们的重点之一。但长期困扰我们的一个问题是,海量非结构化多模态数据作为绝对主体的使用率仍然很低,严重制约了数据要素的流通,面临着日益严峻的安全合规性挑战。为了加快数据要素的流通过程和价值释放,本文提出了一种安全轻量级的海量非结构化多模态数据云计算模型。实现方法包括利用NLP、CNN、图像处理、音频处理技术对非结构化大数据进行多模态特征识别和提取,快速安全的密文计算,利用HE(同态加密)对特征索引数据库进行密文检索,以及多模态数据融合等。通过上述模型和算法,包括HE算法的优化解,验证了海量跨媒体非结构化数据的“可计算但不可见”,证明了其安全性、有效性和时间效率。
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