Data analysis
Results
All (12)
All (12) (0 to 10 of 12 results)
- Stats in brief: 89-20-00082021001Description: This video is part of the confidentiality vetting support series and presents examples of how to use SAS to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-29
- Stats in brief: 89-20-00082021002Description: This video is part of the confidentiality vetting support series and presents examples of how to use SAS to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021003Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021004Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Stats in brief: 89-20-00082021005Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021006Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Articles and reports: 11-522-X202100100027Description:
Privacy concerns are a barrier to applying remote analytics, including machine learning, on sensitive data via the cloud. In this work, we use a leveled fully Homomorphic Encryption scheme to train an end-to-end supervised machine learning algorithm to classify texts while protecting the privacy of the input data points. We train our single-layer neural network on a large simulated dataset, providing a practical solution to a real-world multi-class text classification task. To improve both accuracy and training time, we train an ensemble of such classifiers in parallel using ciphertext packing.
Key Words: Privacy Preservation, Machine Learning, Encryption
Release date: 2021-10-29 - Articles and reports: 12-001-X202100100003Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24 - Articles and reports: 11-633-X2021003Description:
Canada continues to experience an opioid crisis. While there is solid information on the demographic and geographic characteristics of people experiencing fatal and non-fatal opioid overdoses in Canada, there is limited information on the social and economic conditions of those who experience these events. To fill this information gap, Statistics Canada collaborated with existing partnerships in British Columbia, including the BC Coroners Service, BC Stats, the BC Centre for Disease Control and the British Columbia Ministry of Health, to create the Statistics Canada British Columbia Opioid Overdose Analytical File (BC-OOAF).
Release date: 2021-02-17 - 10. Using personal health insurance numbers to link the Canadian Cancer Registry and the Discharge Abstract Database ArchivedArticles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17
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Analysis (12)
Analysis (12) (0 to 10 of 12 results)
- Stats in brief: 89-20-00082021001Description: This video is part of the confidentiality vetting support series and presents examples of how to use SAS to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-29
- Stats in brief: 89-20-00082021002Description: This video is part of the confidentiality vetting support series and presents examples of how to use SAS to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021003Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021004Description: This video is part of the confidentiality vetting support series and presents examples of how to use Stata to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Stats in brief: 89-20-00082021005Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to create proportion output for researchers working with confidential data.Release date: 2022-04-27
- Stats in brief: 89-20-00082021006Description: This video is part of the confidentiality vetting support series and presents examples of how to use R to perform the dominance and homogeneity test while using the Census.Release date: 2022-04-27
- Articles and reports: 11-522-X202100100027Description:
Privacy concerns are a barrier to applying remote analytics, including machine learning, on sensitive data via the cloud. In this work, we use a leveled fully Homomorphic Encryption scheme to train an end-to-end supervised machine learning algorithm to classify texts while protecting the privacy of the input data points. We train our single-layer neural network on a large simulated dataset, providing a practical solution to a real-world multi-class text classification task. To improve both accuracy and training time, we train an ensemble of such classifiers in parallel using ciphertext packing.
Key Words: Privacy Preservation, Machine Learning, Encryption
Release date: 2021-10-29 - Articles and reports: 12-001-X202100100003Description:
One effective way to conduct statistical disclosure control is to use scrambled responses. Scrambled responses can be generated by using a controlled random device. In this paper, we propose using the sample empirical likelihood approach to conduct statistical inference under complex survey design with scrambled responses. Specifically, we propose using a Wilk-type confidence interval for statistical inference. Our proposed method can be used as a general tool for inference with confidential public use survey data files. Asymptotic properties are derived, and the limited simulation study verifies the validity of theory. We further apply the proposed method to some real applications.
Release date: 2021-06-24 - Articles and reports: 11-633-X2021003Description:
Canada continues to experience an opioid crisis. While there is solid information on the demographic and geographic characteristics of people experiencing fatal and non-fatal opioid overdoses in Canada, there is limited information on the social and economic conditions of those who experience these events. To fill this information gap, Statistics Canada collaborated with existing partnerships in British Columbia, including the BC Coroners Service, BC Stats, the BC Centre for Disease Control and the British Columbia Ministry of Health, to create the Statistics Canada British Columbia Opioid Overdose Analytical File (BC-OOAF).
Release date: 2021-02-17 - 10. Using personal health insurance numbers to link the Canadian Cancer Registry and the Discharge Abstract Database ArchivedArticles and reports: 82-003-X201500614196Description:
This study investigates the feasibility and validity of using personal health insurance numbers to deterministically link the CCR and the Discharge Abstract Database to obtain hospitalization information about people with primary cancers.
Release date: 2015-06-17
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