The Applicability of the Perturbation Model-based Privacy Preserving Data Mining for Real-world Data

2006 ◽  
Author(s):  
Li Liu ◽  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Real World Data ◽  
Privacy Preserving Data Mining ◽  
World Data ◽  
Perturbation Model ◽  
Model Based

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Cyber Crime ◽  
2013 ◽  
pp. 395-415 ◽  
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

scholarly journals An application of machine learning based on real-world data: Mining features of fibrinogen in clinical stages of lung cancer between sexes

2021 ◽  
Vol 9 (8) ◽  
pp. 623-623
Author(s):  
Fangtao Yin ◽  
Hongyu Zhu ◽  
Songlin Hong ◽  
Chen Sun ◽  
Jie Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Data Mining ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Clinical Stages

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and its Application to (Vertically) Partitioned Spatio-Temporal Data

2013 ◽  
pp. 45-65
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

scholarly journals Median age at HPV infection among women in the United States: A model-based analysis informed by real-world data

2021 ◽  
Author(s):  
Vimalanand S Prabhu ◽  
Craig S Roberts ◽  
Smita Kothari ◽  
Linda Niccolai
Keyword(s):  
Natural History ◽  
Real World ◽  
Clinical Decision Making ◽  
Age Distribution ◽  
The United States ◽  
Hpv Infection ◽  
Real World Data ◽  
World Data ◽  
Model Based ◽  
Model Based Analysis

Abstract Background The US Advisory Committee for Immunization Practices (ACIP) recommended shared clinical decision-making for HPV vaccination of individuals aged 27 to 45 years (mid-adults) in June 2019. Determining the median age at causal HPV infection and CIN2+ diagnosis based on the natural history of HPV disease can help better understand the incidence of HPV infections and the potential benefits of vaccination in mid-adults. Methods Real-world data on CIN2+ diagnosis from the pre-vaccine era were sourced from a statewide surveillance registry in Connecticut. Age distribution of CIN2+ diagnosis in 2008 and 2009 was estimated. A discrete-event simulation model was developed to predict the age distribution of causal HPV infection. The optimal age distribution of causal HPV infection provided the best goodness-of-fit statistic to compare the predicted vs real-world age distribution of CIN2+ diagnosis. Results The median age at CIN2+ diagnosis from 2008 through 2009 in Connecticut was 28 years. The predicted median age at causal HPV infection was estimated to be 23.9 years. There was a difference of 5.2 years in the median age at acquisition of causal HPV infection and the median age at CIN2+ diagnosis. Conclusions Real-world data on CIN2+ diagnosis and model-based analysis indicate a substantial burden of infection and disease among women aged 27 years or older, which supports the ACIP recommendation to vaccinate some mid-adults . When natural history is known, this novel approach can also help determine the timing of causal infections for other commonly asymptomatic infectious diseases.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

2011 ◽  
Vol 7 (1) ◽  
pp. 46-66 ◽  
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

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