Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Qi, Lianyong; Hu, Chunhua; Zhang, Xuyun; Khosravi, Mohammad R.; Sharma, Suraj; Pang, Shaoning; Wang, Tian

doi:10.1109/tii.2020.3012157

Cited by 212 publications

(130 citation statements)

References 29 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…(3) Third, how to further fuse different privacy protection solutions [28][29][30][31] for better performances is still an open problem that calls for future study.…”

Section: Further Discussionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

Self Cite

View full text Add to dashboard Cite

With the advent of Internet of Health (IoH) age, traditional medical or healthy services are gradually migrating to the Web or Internet and have been producing a considerable amount of medical data associated with patients, doctors, medicine, medical infrastructure and so on. Effective fusion and analyses of these IoH data are of positive significances for the scientific disaster diagnosis and medical care services. However, IoH data are often distributed across different departments and contain partial user privacy. Therefore, it is often a challenging task to effectively integrate or mine the sensitive IoH data, during which user privacy is not disclosed. To overcome the above difficulty, we put forward a novel multi-source medical data integration and mining solution for better healthcare services, named PDFM (Privacy-free Data Fusion and Mining). Through PDFM, we can search for similar medical records in a time-efficient and privacy-preserving manner, so as to offer patients with better medical and health services. A group of experiments are enacted and implemented to demonstrate the feasibility of the proposal in this work.

show abstract

“…(3) Third, how to further fuse different privacy protection solutions [28][29][30][31] for better performances is still an open problem that calls for future study.…”

Section: Further Discussionmentioning

confidence: 99%

Multi-Source Medical Data Integration and Mining for Healthcare Services

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the experiments, we did not measure the performance of privacy guaranteeing due to the characteristics of hash technology [26][27][28]. In addition, only the prediction accuracy and efficiency are compared in the evaluation section.…”

Section: Further Discussionmentioning

confidence: 99%

Secure User Privacy in Population Physique Clustering and Prediction Based on Sport Questionnaires

et al. 2020

View full text Add to dashboard Cite

Population physique is one of the key aspects for measuring and evaluating the healthy degree or living level of the population of a nation. Many population physique evaluation models or tools have been developed in the past decades, among which questionnaire is an essential and promising way to help achieve the above population physique measurement goal. Typically, through designing, distributing and collecting a variety of sport questionnaires associated with people's living conditions or sport preferences, we can quantify, observe and cluster the healthy degree of the whole nation's population objectively and scientifically. However, the above sport questionnaire-based population physique analysis methods are often time-consuming as a considerable amount of sport questionnaires needs to be compared. Moreover, the sport questionnaires filled out by people often contains some sensitive information that is not supposed to be disclosed to other people, which also call for appropriate privacy protection measures. Inspired by the above two challenges, we introduce hash techniques into population physique evaluation process and afterwards, we propose a hash-based population physique clustering and prediction method that is efficient in time cost and effective in terms of privacy protection. At last, we designed experiments based on a dataset to prove the effectiveness of our proposal in this research work.

show abstract

“…With the above analyses, missing data can be predicted accordingly. Concrete variants include user-based collaborative filtering [7], item-based collaborative filtering [8] and hybrid collaborative filtering [9].…”

Section: A Missing Data Prediction and Similar Item Clusteringmentioning

confidence: 99%

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Xing³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

With the ever-increasing popularity of sports and health ideas, people are paying more attentions to gaining high-quality healthy life through various taking various sport items or exercises. Through observing and analyzing the past sport exercise score records, we can cluster the players into different categories, each of which share the same or similar sport preferences or performances. However, the sport exercise score records are often massive and often stored in different cloud platforms, which raise a big difficulty for time-efficient player clustering. Furthermore, the sport exercise score records are a kind of privacy for most players; therefore, it is often not rational or legal to release these sensitive data to the public for similar player clustering purpose. Considering the above two issues, we use SimHash, a kind of privacy-aware approximate neighbor search technique, for similar player clustering by analyzing the sport exercise score records distributed across different cloud platforms. Thus, we can realize privacy-aware similar player clustering through SimHash. At last, we provide a set of experiments to validate the advantages of our proposed privacy-aware similar player clustering algorithm. Reported experimental results show the effectiveness of our proposal in remedying the big data volume and privacy concerns in player clustering based on sport exercise score records.

show abstract

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

Cited by 212 publications

References 29 publications

Multi-Source Medical Data Integration and Mining for Healthcare Services

Multi-Source Medical Data Integration and Mining for Healthcare Services

Secure User Privacy in Population Physique Clustering and Prediction Based on Sport Questionnaires

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Contact Info

Product

Resources

About