Privacy-preserving data integration and sharing in multi-party IoT environments: An entity embedding perspective

Abstract

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.