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Population and demography
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Geography | Automotive repair and maintenance | Electronic, commercial and industrial machinery and equipment repair and maintenance |
---|---|---|
percent | ||
Canada | 0.91 | 1.73 |
Newfoundland and Labrador | 3.56 | 1.57 |
Prince Edward Island | 1.95 | 3.80 |
Nova Scotia | 1.95 | 2.59 |
New Brunswick | 0.59 | 1.55 |
Quebec | 2.70 | 4.36 |
Ontario | 1.73 | 4.83 |
Manitoba | 1.46 | 4.83 |
Saskatchewan | 2.00 | 5.23 |
Alberta | 0.89 | 2.68 |
British Columbia | 2.04 | 2.95 |
Yukon | 0.61 | 0.47 |
Northwest Territories | 0.00 | 0.00 |
Nunavut | 0.00 | 50.29 |
By: Allie MacIsaac, Statistics Canada
On November 25, 2020, senior managers involved in many facets of data science gathered (virtually, of course!) for the first directors' committee meeting of the Data Science Network for the Federal Public Service. This meeting was an important stepping stone for the Network, as it continues to grow and expand its reach in the public service and beyond.
Participants represented a broad range of departments and partners: Bank of Canada; Canada Border Services Agency; Environment and Climate Change Canada; Department of National Defense; Employment and Social Development Canada; Industry Canada; Immigration, Refugee and Citizenship Canada; Public Health Agency of Canada; National Research Council Canada; Natural Resources Canada; Privy Council Office; Public Service Commission; Statistics Canada; Shared Services Canada; Transport Canada; and Treasury Board of Canada Secretariat.
Statistics Canada, the agency spearheading the network, conducted the meeting. Opening remarks were provided by André Loranger, Assistant Chief Statistician of Analytical Studies, Methodology and Statistical Infrastructure at Statistics Canada. André noted that "the federal public service needs to adapt now to address society's growing need for timely analytical outputs to support Canadians and policy makers."
The opening remarks were followed by a presentation featuring the network's goals, objectives and areas of focus.
The goal of the Data Science Network is to establish the foundations of a public-service-wide data science ecosystem.
How will this be achieved? The Network aims to:
The next portion of the presentation focused on the governance and sustainability plan and included a discussion of the funding model and the plan to schedule regular directors' committee meetings. A draft copy of the Network's Terms of Reference was also provided to participants, which outlines the committee's membership and mandate.
This was followed by a lively discussion about the Network's five areas of focus:
The meeting ended with a discussion about the upcoming 2021 Data Conference and how the Network could contribute to this important event (including leading panels and workshops).
The members were eager to discuss next steps, and have already begun to share resources and ideas.
The group looks forward to reconvening in March 2021 and continuing to work together to advance data science in the Government of Canada and beyond. For more information on how your organization can get involved in the Data Science Network, please email statcan.dsnfps-rsdfpf.statcan@statcan.gc.ca.
By: Zachary Zanussi, Statistics Canada
As an organization, StatCan has always strived to adopt new technologies in a timely manner and innovate with methods. Big data technologies such as deep learning have increased the utility of data exponentially, and cloud computing has been an enabling vehicle for this, in particular when working with unclassified data. However, computations of unencrypted sensitive data in a cloud environment may provide exposure to confidentiality threats and cyber-security attacks. Statistics Canada has strict privacy policy measures that have been developed over decades of collecting data and releasing official statistics. To address the new requirements for operating in the cloud, we consider a class of new cryptographic techniques called Privacy Preserving Technologies (PPTs) that might help increase utility by taking greater advantage of technologies such as the cloud or machine learning while continuing to preserve the agency's security posture. This post provides a brief introduction to a number of these PPTs.
What do we mean by privacy? Privacy is the right of individuals to control or influence what information related to them may be collected, used and stored and by whom, and to whom that information may be disclosed. As Canada's national statistical organization, most of the data used at Statistics Canada are provided by respondents, such as an individual or a business. The confidentiality of the data is safeguarded according to the five safe principles to protect the privacy of these respondents by ensuring that the data they provide can't be traced back to them directly or from statistical outputs. More information on Statistics Canada's approach to privacy can be found in the Statistics Canada Trust Centre.
A breach of information involves an attacker successfully reidentifying a response and attributing it back to a particular respondent; in the case that the respondent is a person, then it is also called a breach of privacy. In this article, we will mainly use the terminology of privacy, while keeping in mind that these technologies, applied correctly, protect the data of any kind of respondent.
The respondents' data are considered as the input to some statistical process that produces an output. If an attacker gains access to the input data, then it's a breach of input privacy, while if the attacker can reverse engineer private data from the output, then it's a breach of output privacy. It is possible to prevent both of these types of breaches using classical statistical methods such as anonymizing, where potentially attributing features of data are removed; or perturbation, where the data values are modified in some way to prevent accurate reidentification. The downside is that these classical methods necessarily sacrifice the utility of the data, in particular sensitive data. Moreover, there are numerous reidentification examples which prove that these traditional techniques do not necessarily provide desired cryptographic security guaranteesNote de bas de page 1, Note de bas de page 2. The goal is to leverage PPTs to maintain strong privacy attributes while preserving as much utility as possible. The end result is effectively enhancing utility in the privacy vs. utility equation.
The output privacy of respondents is protected by carefully controlling the results of aggregate statistics. For example, it's possible for an adversary to reconstruct input data through the careful analysis of published statistics. Alternatively, if the public has query access to a secure database, where someone could request simple statistics (mean, max, min, etc.) on subsets of the database, an adversary could abuse this system to extract input data. Differential privacy reduces this risk through the addition of noise to input or output data. At first glance, this is simply an example of the data perturbation that has been employed in official statistics for decades. The innovation is the rigorous mathematical formulation of differential privacy, allowing to precisely gauge exactly where on the "Privacy—Utility" scale an algorithm resides using a parameter ε, called "epsilon."
An algorithm is called ε-differentially private if running the algorithm on two databases that differ by exactly one entry produces results that differ by less than ε. Informally, this means that an adversary querying the same statistic from differing subsets of a database can only infer an amount of information from the database that is bounded by ε. In practice, before releasing statistics, the level of privacy required is determined and used to set ε. Then, random noise is added to the data until the algorithms or statistics to be computed are ε-differentially private. Using differential privacy, better guarantees output privacy while maximizing utility.
Private computation is a blanket term covering a number of different frameworks for computing on data securely. For example, suppose you hold private data that you'd like to perform some sort of computation on, but you don't have access to a secure computing environment—you might be interested in homomorphic encryption. Or suppose you and several peers would like to perform a shared computation on your data without sharing it amongst yourselves—secure multiparty computation might be just what you are looking for. These two secure computing paradigms will be examined in more detail below.
With recent advances in cloud computing, individuals and organizations have unprecedented access to powerful and affordable cloud computing environments. However, most cloud providers do not guarantee the security of data while they are being processed, which means that the cloud is still out of reach to many organizations in possession of highly sensitive private data. Homomorphic encryption (HE) can change this. While traditional encryption algorithms require data to be decrypted before and after use (encryption-at-rest), in HE computations can be performed directly on encrypted data. The results of the computation can only be revealed after decryption. Thus, a data holder can encrypt their data and send them to the cloud knowing that it is cryptographically protected. The cloud can perform the desired computation homomorphically and return the encrypted result, which only the data holder can decrypt and view. In this way, the client can delegate its computations to the cloud without having to rely on trust that their data will be protected; it is secured by encryption! The downside of HE is increased computational complexity, which can be orders of magnitude larger than the corresponding unencrypted computation.
Suppose a number of hospitals have data about patients with a rare disease. If they pool their data, they could run some computations that would help them with treatment and prevention strategies. Laws in many countries require medical institutions to protect their patients' medical data. In the past, the only solution to this problem would be to have all the hospitals agree on a single trusted authority who would collect the data and run the computation. Today, the hospitals could implement (secure) multiparty computation (MPC). With MPC, the hospitals can collaborate and jointly perform their computations without sharing their input data with any party, dispensing the need for a trusted authority, in such a way that their input privacy is guaranteed even in the face of "dishonest" hospitals. MPC protocols are usually implemented using multiple rounds of "secret sharing," where each party holds a piece of a smaller computation that they use to perform their larger computation. The downside of MPC is increased computational complexity (although, usually not as much as HE) and the fact that the protocols usually entail multiple rounds of interactive communication.
Neural networks and artificial intelligence may be the two technologies that have thrived the most in the era of big data. Rather than write a program to complete a task, data are fed into a machine and it trains a model to perform the task. This makes data collection the most important part of the process. As discussed above, this collection process can be prohibitive when the data are distributed and sensitive. Distributed learning is a class of MPC protocols that aim to train a model on data that is owned by multiple parties who want to keep their data private. Two protocols that implement this process in slightly different ways, known as Federated Learning and Split Learning will be discussed. The remainder of this section will assume a basic knowledge of how to train a neural network.
Both of these protocols begin with the same setup; multiple parties have access to data that they consider sensitive, and there is a central untrusted authority server who will assist them. The parties agree on a neural network architecture they would like to train, as well as other particulars such as hyperparameters. Here the two ideas diverge.
In federated learning, each party holds an identical local copy of the network they are training. They each perform one epoch of training on their network and send their gradients to the authority. The authority coordinates these gradients and instructs each of the parties on how to update their local models by combining the insights gained by every party's data. The process then repeats for the desired number of epochs, where finally the authority and every party holds a trained version of the network that they can use however they see fit. The resulting networks are identical, and the process reveals no more about the data than the accumulated gradients computed by each party. This could potentially facilitate an avenue for attack that needs to be considered when implementing a federated learning scheme.
In split learning, the neural net is split by the authority at a certain layer, and layers after the split are shared with the parties. Each party propagates their data up to the cut, then sends the activations at the cut layer to the server. The server finishes the forward propagation on the rest of the network, performs backward propagation up to the cut, then sends the gradients to the parties who can then each finish back propagation and update their copy of the network. After the desired number of epochs, the authority distributes its half of the network to each of the parties, and then each party has their own copy of the total network, the bottom half of each being explicitly tailored to their data. The only data leaked are those which can be inferred from the activations and gradients exchanged at each epoch. The layers below the split serve to alter the data enough that they are protected (sometimes called "smashing" the data), while still allowing the server to gather insights from it.
This article discussed a number of emerging privacy preserving technologies and how they can improve the utility extracted from data without sacrificing the privacy of those providing it. Future posts will take a more in-depth look at some of these technologies, so stay tuned! Next up is a much closer look at homomorphic encryption, from the mathematics of lattices to applications.
Want to keep in the loop about these emerging technologies, or want to share your work in the field of privacy? Check out our Privacy Preserving Technologies Community of Practice GCConnex page to discuss these DSN privacy posts, connect with peers interested in privacy, and share resources and ideas with the community. You can also give feedback on this post or leave suggestions for future posts in this series.
Forecast results 2020-21 | Planned results 2021-22 | |
---|---|---|
Expenses | ||
Statistical information
|
773,547 | 977,075 |
Internal services
|
94,092 | 71,099 |
Total expenses | 867,639 | 1,048,174 |
Revenues | ||
Special statistical services
|
129,403 | 138,000 |
Other revenues
|
100 | 100 |
Revenues earned on behalf of the Government of Canada
|
-16,346 | -18,100 |
Total revenues | 113,157 | 120,000 |
Net cost of operations before government funding and transfers | 754,482 | 928,174 |
The accompanying notes form an integral part of the Future-Oriented Statement of Operations
The Future-Oriented Statement of Operations has been prepared on the basis of government priorities and the plans of Statistics Canada (the agency) as described in the 2021-22 Departmental Plan.
The information in the forecasted results for fiscal year 2020-21 is based on actual results as at December 30, 2020 and on forecasts for the remainder of the fiscal year. Forecasts were estimated for the planned results for fiscal year 2021-22.
The main assumptions underlying the forecasts are as follows:
These assumptions are made as at December 30, 2020.
Although every attempt has been made to forecast final results for the remainder of 2020-21 and for 2021-22, actual results achieved for both years are likely to differ from the forecasted information presented, and this variation could be material.
In preparing this Future-Oriented Statement of Operations, the agency established estimates and assumptions about the future. These estimates and assumptions may differ from the subsequent actual results. Estimates and assumptions are based on past experience and other factors, including expectations of future events that are believed to be reasonable under the circumstances, and are continually evaluated.
Factors that could lead to material differences between the Future-Oriented Statement of Operations and the historical financial statements include:
After the Departmental Plan is tabled in Parliament, the agency will not be updating the forecasts for any changes in financial resources made in ensuing supplementary estimates. Variances will be explained in the Departmental Results Report.
The Future-Oriented Statement of Operations has been prepared using the Government of Canada's accounting policies in effect for fiscal year 2020-21, and is based on Canadian public sector accounting standards. The presentation and results using the stated accounting policies do not result in any significant differences from Canadian public sector accounting standards.
Significant accounting policies are as follows:
Transfer payments are recorded as an expense in the year the transfer is authorized and all eligibility criteria have been met by the recipient.
Other expenses are generally recorded when goods are received or services are rendered and include expenses related to personnel, professional and special services, repair and maintenance, utilities, materials and supplies, as well as amortization of tangible capital assets. Provisions to reflect changes in the value of assets or liabilities, such as provisions for bad debts, advances and inventory obsolescence, as well as utilization of inventories and prepaid expenses, and other are also included in other expenses.
Funds received for special statistical services are recorded upon receipt as deferred revenue. These revenues are recognized in the period in which the related expenses are incurred.
Deferred revenue consists of amounts received in advance of the delivery of goods and rendering of services that will be recognized as revenue in a subsequent fiscal year as it is earned.
Other revenues are recognized in the period the event giving rise to the revenues occurred.
Revenues that are non-respendable are not available to discharge the agency's liabilities. Although the deputy head is expected to maintain accounting control, he has no authority over the disposition of non-respendable revenues. As a result, non-respendable revenues are considered to be earned on behalf of the Government of Canada and are therefore presented as a reduction of the agency's gross revenues.
The agency is financed in part by the Government of Canada through parliamentary authorities. Financial reporting of authorities provided to the agency differs from financial reporting according to generally accepted accounting principles because authorities are based mainly on cash flow requirements. Items recognized in the Future-Oriented Statement of Operations in one year may be funded through parliamentary authorities in prior, current or future years. Accordingly, the agency has a different net cost of operations for the year on a government funding basis than on an accrual accounting basis. The differences are reconciled in the following tables:
Forecast results 2020-21 | Planned results 2021-22 | |
---|---|---|
Net cost of operations before government funding and transfers | 754,482 | 928,174 |
Adjustments for items affecting net cost of operations but not affecting authorities: | ||
Amortization of tangible capital assets
|
-28,106 | -24,975 |
Loss on disposal of tangible capital assets
|
-56 | 0 |
Services provided without charge by other federal government departments
|
-107,298 | -108,584 |
Increase/decrease in vacation pay and compensatory leave
|
-17,708 | 7,014 |
Increase in employee future benefits
|
-906 | -1,847 |
Refunds of previous years expenditures
|
712 | 712 |
Consumption of prepaid expenses
|
-13,181 | -12,061 |
Bad debt expense
|
-3 | 0 |
Accrued salary receivables
|
187 | 0 |
Total items affecting net cost of operations but not affecting authorities
|
-166,359 | -139,741 |
Adjustments for items not affecting net cost of operations but affecting authorities: | ||
Acquisitions of tangible capital assets
|
30,863 | 29,266 |
Increase in prepaid expenses
|
12,795 | 13,261 |
Transfer of payment in arrears
|
5 | 0 |
Payments for pay equity settlement
|
140 | 140 |
Total items not affecting net cost of operations but affecting authorities
|
43,803 | 42,667 |
Requested authorities forecasted to be used | 631,926 | 831,100 |
Forecast results 2020-21 | Planned results 2021-22 | |
---|---|---|
Authorities requested | ||
Vote 1: operating expenditures
|
586,646 | 748,327 |
Statutory amounts
|
79,842 | 82,773 |
Total authorities requested | 666,488 | 831,100 |
Less: Estimated unused authorities and other adjustments
|
-34,562 | 0 |
Requested authorities forecasted to be used | 631,926 | 831,100 |
Thank you for taking a few minutes to participate in the 2021 Census. The information you provide is converted into statistics used by communities, businesses and governments to plan services and make informed decisions about employment, education, health care, market development and more.
Your answers are collected under the authority of the Statistics Act and kept strictly confidential. By law, every person must complete a 2021 Census of Population questionnaire.
Statistics Canada makes use of existing sources of information such as immigration, income tax and benefits data to ensure the least amount of burden is placed on households.
The information that you provide may be used by Statistics Canada for other statistical and research purposes or may be combined with other survey or administrative data sources.
Make sure you count yourself into Canada's statistical portrait, and complete your census questionnaire today.
Thank you,
Anil Arora
Chief Statistician of Canada
Français au verso
This information is collected under the authority of the Statistics Act, R.S.C. 1985, c. S-19.
1. What is your telephone number?
2. What is the address where you received this questionnaire?
3. What is the mailing address of this dwelling, if different from above?
(e.g., Rural Route, PO Box, General Delivery)
1. What is your name?
The following questions refer to your situation on May 11, 2021.
If you are:
If you are:
Is this your main residence?
STOP HERE.
You should be included on the household questionnaire for the above address.
See instructions in Step E on page 6.
1. a) Is there any other address in Canada where someone may include you on the census form for that household?
For example, at the home of a parent, relative or friend, or a place where you live while working, or a vacation home.
1. b) Please give the name of another adult (if any) living at this other address.
2. What was your sex at birth?
Sex refers to sex assigned at birth.
3. What is your gender?
Refers to current gender which may be different from sex assigned at birth and may be different from what is indicated on legal documents.
4. What are your date of birth and age?
If exact date of birth is not known, enter best estimate. For children less than 1 year old, enter 0 for age.
5. What is your marital status?
Mark "x" one circle only.
6. Are you living with a common-law partner?
Common-law refers to two people who live together as a couple but who are not married, regardless of the duration of the relationship.
7. What is your status here (at the address you entered on the front cover)?
Mark “x” or specify one response only.
8. Can you speak English or French well enough to conduct a conversation?
Mark "x" one circle only.
9. a) What language(s) do you speak on a regular basis at home?
If you indicate only one language in question 9. a), go to question 10.
9. b) Of these languages, which one do you speak most often at home?
Indicate more than one language only if they are spoken equally at home.
10. What is the language that you first learned at home in childhood and still understand?
If you no longer understand the first language learned, indicate the second language learned.
11. Have you ever served in the Canadian military?
Canadian military service includes service with the Regular Force or Primary Reserve Force as an Officer or Non-Commissioned Member. It does not include service with the Cadets (COATS), the Supplementary Reserve or the Canadian Rangers.
Mark "x" one circle only.
The following questions collect information in accordance with the Canadian Charter of Rights and Freedoms to support education programs in English and French in Canada.
12. Is this dwelling located in Quebec?
13. Did you do any of your primary or secondary schooling in French in Canada (including immersion)?
Mark "x" one circle only.
14. In which type of program was this schooling in French done?
15. For how many years did you attend a regular French program in a French-language school?
16. Did you do any of your primary or secondary schooling in an English-language school in Canada (including immersion)?
Mark "x" one circle only.
17. For how many years did you do your schooling in an English-language school in Canada (including immersion)?
You have now completed your questionnaire.
Please see instructions on the envelope.
Thank you for your cooperation.
The confidentiality of your responses is protected by law. All Statistics Canada employees have taken an oath of secrecy. Your personal information cannot be given to anyone outside Statistics Canada without your consent. This is your right.
Steps A to D and question 1 are used to collect contact information and determine who should be included on the questionnaire. They help us ensure that we have counted everyone we need to count and that no one is counted twice.
Questions 2 to 7 provide information about the living arrangements of people in Canada, the family size, the number of children living with one parent or two parents, and the number of people who live alone. This information is used for planning social programs, such as Old Age Security and the Canada Child Benefit. It is also used by municipalities to plan a variety of services such as day care centres, schools, police, fire protection and residences for seniors.
Questions 8 to 10 are used to provide a profile of the linguistic diversity of Canada's population. This information is used to estimate the need for services in English and French, and to better understand the current state and the evolution of Canada's various language groups.
Question 11 provides information on the number of people with Canadian military experience. Governments will use this information to develop programs and services to meet the changing needs of the Veteran population.
Questions 12 to 17 collect information in accordance with the Canadian Charter of Rights and Freedoms to support education programs in English and French in Canada.
Please use the space provided below if you have concerns, suggestions or comments to make about:
Thank you for taking a few minutes to participate in the 2021 Census. The information you provide is converted into statistics used by communities, businesses and governments to plan services and make informed decisions about employment, education, health care, market development and more.
Your answers are collected under the authority of the Statistics Act and kept strictly confidential. By law, every person must complete a 2021 Census of Population questionnaire.
Statistics Canada makes use of existing sources of information such as immigration, income tax and benefits data to ensure the least amount of burden is placed on households.
The information that you provide may be used by Statistics Canada for other statistical and research purposes or may be combined with other survey or administrative data sources.
To ensure confidentiality, put your completed questionnaire in the envelope provided and seal it. Return the envelope in accordance with the instructions given by your unit or department. Only Statistics Canada staff will open the envelopes.
Make sure you count yourself into Canada's statistical portrait, and complete your census questionnaire today.
Thank you,
Anil Arora
Chief Statistician of Canada
Visit www.census.gc.ca
Ce questionnaire est disponible en français
This information is collected under the authority of the Statistics Act, R.S.C. 1985, c. S-19.
1. Do you have a permanent place of residence in Canada presently occupied by one or more members of your family?
2. Enter the name and address of the military or government establishment outside Canada to which you are presently attached or, if none, the city or town and the country in which you reside.
1. Including yourself, how many persons usually live at your address (outside Canada) as of May 11, 2021?
Include: all persons who have their main residence at this address, even if they are temporarily away, provided they are:
2. Including yourself, list all persons who usually live at your address (outside Canada).
Important: Begin the list with an adult followed, if applicable, by that person's spouse or common-law partner and by their children. Continue with all other persons who usually live at this address.
Copy the names in Step B to question 1, at the top of page 4.
Keep the same order.
If more than six persons live at your address (outside Canada), you will need an extra questionnaire. A second questionnaire may be obtained from the representative or official who delivered this one.
1. Name
In the spaces provided, copy the names in the same order as in Step B. Then answer the following questions for each person.
Person 1
The following questions refer to each person's situation on May 11, 2021, unless otherwise specified.
2. What was this person's sex at birth?
Sex refers to sex assigned at birth.
3. What is this person's gender?
Refers to current gender which may be different from sex assigned at birth and may be different from what is indicated on legal documents.
4. What are this person's date of birth and age?
If exact date of birth is not known, enter best estimate. For children less than 1 year old, enter 0 for age.
5. What is this person's marital status?
Mark "x" one circle only.
6. Is this person living with a common-law partner?
Common-law refers to two people who live together as a couple but who are not married, regardless of the duration of the relationship.
7. What is the relationship of this person to Person 1?
If none of the responses in the list describes this person's relationship to Person 1, then specify a response under "Other relationship".
Person 1
Person 2
Persons 3-6
8. Can this person speak English or French well enough to conduct a conversation?
Mark "x" one circle only.
9. a) What language(s) does this person speak on a regular basis at home?
If this person indicates only one language in question 9. a), go to question 10.
9. b) Of these languages, which one does this person speak most often at home?
Indicate more than one language only if they are spoken equally at home.
10. What is the language that this person first learned at home in childhood and still understands?
If this person no longer understands the first language learned, indicate the second language learned.
11. Has this person ever served in the Canadian military?
Canadian military service includes service with the Regular Force or Primary Reserve Force as an Officer or Non-Commissioned Member. It does not include service with the Cadets (COATS), the Supplementary Reserve or the Canadian Rangers.
Mark "x" one circle only.
The following questions collect information in accordance with the Canadian Charter of Rights and Freedoms to support education programs in English and French in Canada.
12. Is the address in Canada you provided in Step A, question 1, located in Quebec?
13. Did this person do any of their primary or secondary schooling in French in Canada (including immersion)?
Mark "x" one circle only.
14. In which type of program was this schooling in French done?
15. For how many years did this person attend a regular French program in a French-language school?
16. Did this person do any of their primary or secondary schooling in an English-language school in Canada (including immersion)?
Mark "x" one circle only.
17. For how many years did this person do their schooling in an English-language school in Canada (including immersion)?
Please use the space provided below if you have concerns, suggestions or comments to make about:
If more than six persons live at your address (outside Canada), you will need an extra questionnaire. A second questionnaire may be obtained from the representative or official who delivered this one.
You have now completed your questionnaire. Please return it in accordance with instructions from your unit or department.
Thank you for your cooperation.
Steps A and B and question 1 are used to collect contact information and determine who should be included on the questionnaire. They help us ensure that we have counted everyone we need to count and that no one is counted twice.
Questions 2 to 7 provide information about the living arrangements of people in Canada, the family size, the number of children living with one parent or two parents, and the number of people who live alone. This information is used for planning social programs, such as Old Age Security and the Canada Child Benefit. It is also used by municipalities to plan a variety of services such as day care centres, schools, police, fire protection and residences for seniors.
Questions 8 to 10 are used to provide a profile of the linguistic diversity of Canada's population. This information is used to estimate the need for services in English and French, and to better understand the current state and the evolution of Canada's various language groups.
Question 11 provides information on the number of people with Canadian military experience. Governments will use this information to develop programs and services to meet the changing needs of the Veteran population.
Questions 12 to 17 collect information in accordance with the Canadian Charter of Rights and Freedoms to support education programs in English and French in Canada.
The confidentiality of your responses is protected by law. All Statistics Canada employees have taken an oath of secrecy. Your personal information cannot be given to anyone outside Statistics Canada without your consent. This is your right.
Statistics Canada is conducting the Canadian COVID-19 Antibody and Health Survey (CCAHS) from November 2020 to March 2021. This survey collects health data on the current COVID-19 pandemic by asking selected participants to complete an electronic questionnaire and provide a blood sample from a self-administered finger prick (dried blood spot sample). This voluntary survey will be sent to approximately 48,000 Canadians, aged 1 and over, across the provinces and territories. It is expected that approximately 20,000 respondents will complete the entire survey (questionnaire and blood sample).
All processes of the CCAHS have been reviewed and approved by the Health Canada/Public Health Agency of Canada Research Ethics Board to ensure that internationally recognized ethical standards for human research are met and maintained.
A privacy impact assessment for the CCAHS was conducted to determine if there were any privacy, confidentiality or security issues with this survey and to make recommendations to resolve or mitigate any issues.
This survey was developed in consultation with the COVID-19 Immunity Task Force (CITF). The CITF is funded by the Government of Canada and is composed of members from various domains, including universities, hospitals and the public health sector. The CITF was created to support the development and implementation of population-based serological studies (using blood tests) on the SARS-CoV-2 virus in order to better understand the impact of the pandemic in Canada.
Results from this survey will provide important information on the health status of Canadians during the COVID-19 pandemic, including an estimate of the prevalence of infection from SARS-CoV-2, the virus that causes COVID-19. This survey will also provide a platform to explore new measurement techniques, as this is the first time that self-administered blood samples are being collected from a nationally-representative sample of Canadians.
The PIA identifies the level of potential risk (level 1 is the lowest level of potential risk and level 4 is the highest) associated with the following risk areas:
Risk scale | |
---|---|
a) Type of program or activity Program or activity that does not involve a decision about an identifiable individual. |
1 |
b) Type of personal information involved and context Sensitive personal information, including detailed profiles, allegations or suspicions and bodily samples, or the context surrounding the personal information is particularly sensitive. |
4 |
c) Program or activity partners and private sector involvement Private sector organizations, international organizations or foreign governments |
4 |
d) Duration of the program or activity One-time program or activity |
1 |
e) Program population The program's use of personal information is not for administrative purposes. Information is collected for statistical purposes, under the authority of the Statistics Act. |
N/A |
f) Personal information transmission The personal information is transmitted using wireless technologies. |
4 |
g) Technology and privacy The CCAHS does not require the implementation of new technology or modifications to legacy systems to support the creation, collection or handling of personal information. |
|
h) Potential risk that in the event of a privacy breach, there will be an impact on the individual or employee. There is a very low risk for a breach of any personal information being disclosed without proper authorization. The impact on the individual would be high, as it could negatively affect their reputation because of the stigmatization related to certain health conditions or illnesses. |
This assessment of the CCAHS did not identify any privacy risks that cannot be managed using existing Statistics Canada safeguards and procedures, as well as those in place at the various laboratories being used. Any remaining risks are either negligible or are such that Statistics Canada is prepared to accept and manage.