Search Results for author: (Member

Found 5 papers, 0 papers with code

An Efficient Privacy-Preserving Multi-Keyword Query Scheme in Location Based Services

no code implementations IEEE 2020 SHIWEN ZHANG 1, 2, (Member, TINGTING YAO3, WEI LIANG 4, VOUNDI KOE ARTHUR SANDOR4, AND KUAN-CHING LI 5, (Senior Member, IEEE)

In this article, aiming at a multi-keywords query in LBS, we propose a novel efficient and privacy-preserving multi-keyword query scheme (PPMQ) over the outsourced cloud, which satisfies the requirements of the location and query content privacy protection, query efficiency, the confidentiality of LBS data and scalability regarding the data users.

Privacy Preserving

Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics

no code implementations IEEE 2018 WEIDONG MIN, (Member, IEEE), HAO CUI, HONG RAO, ZHIXUN LI, AND LEIYUE YAO

Through measuring the changes of these characteristics and judging the relations between the people and furniture nearby, the falls on furniture can be effectively detected.

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