The increasing prevalence of IoT applications highlights the urgency for insightful data fusion and information acquisition, boosting data integration and sharing needs. However, challenges arise in multi-party data sharing due to inherent data heterogeneity and privacy concerns. To address these issues, this paper discusses the feasibility of using embedding vectors as the semantic representation, aiming to enhance interoperability across diverse data sources and lay the foundation for natural language-based data querying. At the specific method level, this paper proposes an improved entity tree embedding algorithm to reduce information loss and ameliorate the representation of entity semantics. Additionally, a privacy preservation mechanism based on the entity embedding approach is introduced to provide privacy protection for text-based data. Experimental results on address data demonstrate the mechanism’s efficacy in achieving privacy protection comparable to the widely adopted 2D Laplace plane noise method. Furthermore, incorporating the entity tree embedding into the privacy mechanism could yield more robust and reasonable results regarding location privacy and service quality, signifying the validity of the entity embedding results.

中文翻译：

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多方物联网环境中的隐私保护数据集成和共享：实体嵌入视角

物联网应用的日益普及凸显了洞察数据融合和信息获取的紧迫性，从而推动了数据集成和共享需求。然而，由于固有的数据异构性和隐私问题，多方数据共享面临挑战。针对这些问题，本文讨论了使用嵌入向量作为语义表示的可行性，旨在增强跨不同数据源的互操作性，并为基于自然语言的数据查询奠定基础。在具体方法层面，本文提出了一种改进的实体树嵌入算法，以减少信息丢失并改善实体语义的表示。此外，引入了基于实体嵌入方法的隐私保护机制，为基于文本的数据提供隐私保护。地址数据的实验结果表明，该机制在实现隐私保护方面的有效性可与广泛采用的二维拉普拉斯平面噪声方法相媲美。此外，将实体树嵌入纳入隐私机制可以在位置隐私和服务质量方面产生更稳健和合理的结果，表明实体嵌入结果的有效性。