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A hyperspectral unmixing approach for ink mismatch detection in unbalanced clusters

Journal of Information and Intelligence 2024年第2期2卷 177-190页

作者： Faryal Aurooj Nasir Salman Liaquat Khurram Khurshid nor muzlifah mahyuddin Department of Electrical Engineering Institute of Space TechnologyIslamabad 44000Pakistan School of Electrical and Electronic Engineering Universiti Sains MalaysiaPenang 14300Malaysia

With the rapid development of location-based services and online social networks,POI recommendation services considering geographic and social factors have received extensive ***,the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation ***,there is a degree of distrust of the cloud by service *** protect digital assets,service providers encrypt data before outsourcing ***,encryption reduces data availability,making it more challenging to provide POI recommendation services in outsourcing *** privacy-preserving schemes for geo-social-based POI recommendation have been presented,but they have some limitations in supporting group query,considering both geographic and social factors,and query accuracy,making these schemes *** solve this issue,we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users,which are named GSPR-S and ***,we first utilize the quad tree to organize geographic data and the MinHash method to index social ***,we apply BGV fully homomorphic encryption to design some private algorithms,including a private max/min operation algorithm,a private rectangular set operation algorithm,and a private rectangular overlapping detection *** that,we use these algorithms as building blocks in our schemes for efficiency *** to security analysis,our schemes are proven to be secure against the honest-but-curious cloud servers,and experimental results show that our schemes have good performance.

关键词： k-means clustering Gaussian mixture model(GMM) Hyper spectral imaging(HSI) iVision handwritten hyperspectral images dataset(HHID) Document forensics

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