With intelligent terminal devices’widespread adoption and global positioning systems’advancement,Location-based Social Networking Services(LbSNs)have gained considerable *** recommendation mechanism,which revolves a...
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With intelligent terminal devices’widespread adoption and global positioning systems’advancement,Location-based Social Networking Services(LbSNs)have gained considerable *** recommendation mechanism,which revolves around identifying similar users,holds significant importance in *** order to enhance user experience,LbSNs heavily rely on accurate *** mining and analyzing users who exhibit similar behavioral patterns to the target user,LbSNs can offer personalized services that cater to individual ***,trajectory data,a form encompassing various sensitive attributes,pose privacy *** disclosure of users’precise trajectory information can have severe consequences,potentially impacting their daily ***,this paper proposes the Similar User Discovery Method based on Semantic Privacy(SUDM-SP)for trajectory *** approach involves employing a model that generates noise trajectories,maximizing expected noise to preserve the privacy of the original *** users are then identified based on the published noise trajectory ***-SP consists of two key ***,a puppet noise location,exhibiting the highest semantic expectation with the original location,is generated to derive noisesuppressed trajectory ***,a mechanism based on semantic and geographical distance is employed to cluster highly similar users into communities,facilitating the discovery of noise trajectory similarity among *** trials conducted using real datasets,the effectiveness of SUDM-SP,as a recommendation service ensuring user privacy protection is substantiated.
In biomedical data analysis, feature selection is crucial, particularly for high-dimensional datasets where redundant or irrelevant features might affect model performance. Biomedical datasets introduce additional cha...
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In the volatile domain of financial markets, accurately predicting stock volatility is a paramount challenge due to inherent nonlinearity and rapid market changes. Conventional models like GARCH and SVR-GARCH often fa...
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With the rapid development of high-speed mobile network technology and high-precision positioning technology,the trajectory information of mobile users has received extensive attention from academia and industry in th...
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With the rapid development of high-speed mobile network technology and high-precision positioning technology,the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social *** can mine users’trajectories in Location-based Social Networks to obtain sensitive information,such as friendship groups,activity patterns,and consumption ***,mobile users’privacy and security issues have received growing attention in Location-based Social *** is crucial to strike a balance between privacy protection and data *** paper proposes a differential privacy trajectory protection method based on latent interest community detection(DPTP-LICD),ensuring strict privacy protection standards and user data ***,based on the historical trajectory information of users,spatiotemporal constraint information is extracted to construct a potential community strength model for mobile ***,the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory,and their priorities are assigned based on a popularity model.A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data ***,to prevent privacy leakage,we add Laplace and exponential noise in generating preferred hot spots and recommending user interest *** and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’privacy from disclosure.
In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twi...
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In recent years,immense developments have occurred in the field of Artificial Intelligence(AI)and the spread of broadband and ubiquitous connectivity *** has led to the development and commercialization of Digital Twin(DT)*** widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks(DTNs),which orchestrate through the networks of ubiquitous DTs and their corresponding physical *** create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing,computing,and DT *** high volume of user data and the ubiquitous communication systems in DTNs come with their own set of *** most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data ***,currently,there is not enough literature that focuses on privacy and security issues in DTN *** this survey,we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their ***,we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective *** then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the *** also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the ***,we provide some open research issues and problems in the field of DTN privacy and security.
This paper demonstrates Pynapple-G, an open-source library for scalable spatial grouping queries based on Apache Sedona (formerly known as GeoSpark). We demonstrate two modules, namely, SGPAC and DDCEL, that support g...
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This study presents a comprehensive benchmarking of TabLM, a language model derived from DistilBERT, against traditional machine learning models such as Support Vector Machines (SVM), Light Gradient Boosting Machine (...
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The rise of advanced AI technologies, such as ChatGPT, has presented institutions like the Miller Center with significant opportunities to enhance impact and modernize operations. This paper identifies the key challen...
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This research explores adaptive machine translation, focusing on the translation of English-to-Arabic movie subtitles using advanced large language models such as GPT-3.5 Turbo. Utilizing Sentence-Transformer to gener...
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