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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SKILLPoV: Towards Accessible and Effective Privacy Notice for Amazon...

Jingwen Yan (Clemson University), Song Liao (Texas Tech University), Mohammed Aldeen (Clemson University), Luyi Xing (Indiana University Bloomington), Danfeng (Daphne) Yao (Virginia Tech), Long Cheng (Clemson University)

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BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS

Yinggang Guo (State Key Laboratory for Novel Software Technology, Nanjing University; University of Minnesota), Zicheng Wang (State Key Laboratory for Novel Software Technology, Nanjing University), Weiheng Bai (University of Minnesota), Qingkai Zeng (State Key Laboratory for Novel Software Technology, Nanjing University), Kangjie Lu (University of Minnesota)

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