Shuwen Liu (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China), George C. Polyzos (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China and ExcID P.C., Athens, Greece)

We design a privacy-preserving data proxy mechanism within the FIWARE Data Space framework, utilizing searchable encryption to ensure metadata confidentiality. The system is engineered to enable secure and efficient data querying, hiding the queries from the proxy and other data in the proxy from the querying agent. Recognizing the necessity of regulatory compliance, this paper integrates GDPR compliance modules into the FIWARE Data Space architecture, addressing data collection, storage, sharing, and erasure processes to enhance global applicability and regulatory adherence. In essence, we preserve metadata privacy. Experimental evaluations demonstrate the feasibility of the proposed query privacy mechanisms, focusing on metadata confidentiality and system scalability in data-intensive environments.

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Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)

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Aishwarya Jawne (Center for Connected Autonomy & AI, Florida Atlantic University), Georgios Sklivanitis (Center for Connected Autonomy & AI, Florida Atlantic University), Dimitris A. Pados (Center for Connected Autonomy & AI, Florida Atlantic University), Elizabeth Serena Bentley (Air Force Research Laboratory)

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Xuewei Feng (Tsinghua University), Yuxiang Yang (Tsinghua University), Qi Li (Tsinghua University), Xingxiang Zhan (Zhongguancun Lab), Kun Sun (George Mason University), Ziqiang Wang (Southeast University), Ao Wang (Southeast University), Ganqiu Du (China Software Testing Center), Ke Xu (Tsinghua University)

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