I’ll Be Watching You

I’ll Be Watching You

Once you enter public spaces, both physical and virtual, your personal data is captured in a number of ways. From CCTV cameras to social media, find out how your personal data is collected and used.
​Sarah Villeneuve
Policy Analyst
Stephanie Fielding
Policy & Research Analyst
January 21, 2020
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As individuals move throughout public spaces and interact with publicly-available services, they leave behind large amounts of data in a variety of forms. This includes video images and audio recordings, captured via publicly- or privately-owned video cameras, social media data (and metadata related to time and location), and public service databases.

CCTV and Video Surveillance

The use of video surveillance technology to monitor public spaces has increased dramatically since the 1990s, particularly due to the ease of recording and sharing afforded by VHS, DVD, and the Internet. The use of video recording is often subtle enough to go largely unnoticed by those it aims to capture. While it is difficult to estimate the number of CCTV and video surveillance cameras in operation, some efforts have been made to identify and record where cameras exist. For example, SurveillenceRights.ca collects crowdsourced information on the location of existing CCTV cameras and logs them in a publicly-available interactive map.

Closed-Circuit Television (CCTV), a self-contained surveillance system, which transmits a direct feed of its connected security cameras a connected monitor, has been a popular form of video surveillance for many decades. CCTV cameras capture data on individuals in the form of video footage, sometimes accompanied by audio data as well. Footage captured by CCTV is recorded directly on a Digital Video Recorder. This footage cannot be viewed from outside the system. CCTV cameras must be strategically placed in order to capture activity in a specific area. For this reason, many CCTV cameras are fixed to physical infrastructure, such as buildings or light poles.

According to a report on the worldwide installation of video surveillance cameras, the growth of CCTV has been slowing in recent years.[1] However, this has been accompanied with an increase in security cameras which record and transmit footage through a digital stream wirelessly. Since this can be done over the internet, footage can be viewed from anywhere, as long as there is a connection to the security camera. Alongside this, the decreased cost of video recording technology has enabled video surveillance to be adopted widely by both businesses and residents. However, this widespread use of video surveillance has raised privacy concerns.

The Office of the Privacy Commissioner of Canada recognizes that video surveillance in public places presents a challenge to privacy as it subjects everyone to scrutiny.[2] A number of laws have been developed to ensure transparency related to the use of video surveillance, and safeguard individual privacy rights. The Personal Information Protection and Electronic Documents Act (PIPEDA), for example, requires businesses to post signs outside their entrances that alert customers to the use of video surveillance, its purpose, and a contact number so people can find out where they can obtain a copy of any footage that contains their image. However, the extent to which these are enforced and complied with is not universal.

A group of researchers from the University of Toronto discovered[3] that out of hundreds of video surveillance cameras in two Toronto area malls  the Eaton Centre and Square One Shopping Centre  none complied with the minimum standards required by law, including basic signage. These findings indicate the challenges surrounding the rapidly growing use of video surveillance, alongside the lack of appropriate understanding or enforcement of transparency and oversight.

 

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"In May 2019, it was revealed that Toronto Police have been using facial recognition technology to compare 5,000 still images of suspects captured on security cameras to an internal database of approximately 1.5 million mugshots."

Police Monitoring

Canadian law enforcement agencies regard video surveillance as a legitimate tool to detect, deter, and combat crime, and ward off activity.[4] Today, video surveillance systems are employed both for specific occasions (at a specific time and place), such as festivals or other openly public events, and for 24/7 monitoring of public spaces. This ranges from mundane spaces such as parks and streets to public spaces that may require higher levels of security. For example, the Royal Canadian Mounted Police (RCMP) use video surveillance systems cameras to monitor Parliament Hill in Ottawa, as well as the Canada-US border. The widespread use of video surveillance in public spaces eradicates any guarantee of anonymity in public spaces by subjecting everyone to observation.

Facial Recognition

A new development in video surveillance has been the ability to apply facial recognition technology to footage in order to identify individuals. This has had an enormous impact on the scale and capacity of surveillance technology. Traditionally, video surveillance systems that  used tapes to record footage (such as CCTV) required an operator to manually analyze each event and identify individuals. Today, facial recognition technology can be used to automatically assess and identify individuals from a vast stream of images, even linking disparate sources of video data. Moreover, video images are more likely to be retained for further data mining due to the increase in computer storage and management software.

In May 2019, it was revealed that Toronto Police have been using facial recognition technology[5] to compare 5,000 still images of suspects captured on security cameras to an internal database of approximately 1.5 million mugshots. Matches were generated from 60 percent of the images retrieved from security cameras by the police. This technology, and the use of video surveillance footage, was being used without widespread public awareness.[6] Toronto’s Chief of Police, Mark Saunders, claims the use of this technology, coupled with the increase of video surveillance cameras, has generated breakthroughs in identifying perpetrators of crimes.

The use of facial recognition has been met with contention in other jurisdictions. In May 2019, San Francisco banned the use of facial recognition[7] by police and other law enforcement agencies. This decision has been followed by a number of similar bans in other jurisdictions across the United States.  

"...law enforcement officials can create fake accounts and pose as members of the community they are seeking to gather information on."

Social Media, Protests, and Public Gatherings

Alongside video surveillance, social media data is widely used to monitor activity in public spaces, particularly in cases of protests and large public gatherings. Social media serves a variety of purposes in relation to protests and public gatherings. It is a medium used to help facilitate organization and planning, communicate messages, and attract participants. During an event, such as a protest, it can also be used to share real-time developments and events. The data generated by these actions has been used by law enforcement and other actors to monitor events. This includes metadata, such as location[8] and date and time stamps, associated with user generated content to help identify threats to public safety or cracking down on public demonstrations.

In 2014, the Royal Canadian Mounted Police (RCMP) gathered information from local divisions on prominent Aboriginal activists. The project, known as Project SITKA, combined this information with social media data and identified 313 activists, 89 of whom were deemed to pose a “criminal threat to Aboriginal public order events.”[9] The RCMP developed profiles of these individuals for front-line officers.[10] These profiles included information such as the individual’s name and aliases, date of birth, phone number, email address, vehicle, and organizational affiliations, as well as their mobility throughout Canada and demonstrations they had attended in the previous 5 years.[11] Each individual was also assigned one of three categories: passive, disruptive, or volatile.[12] 

Individuals have tried to counter suspected law enforcement surveillance. In 2016, more than 1 million people checked-in on Facebook to the Standing Rock Indian Reservation in an attempt to confuse and overwhelm law enforcement officials using the social media site to identify and target protesters who had gathered on the site in opposition to the construction of the Dakota Access pipeline.[13] However, it’s unclear what impact this form of obfuscation had. While local police denied the use of Facebook check-ins to monitor protesters, reporters called attention to other tools that could be deployed to track those on the ground, such as Geofeedia, an online aggregator of data from nine social media networks.[14] [15]

In some cases, use of social media by law enforcement officials has been more difficult for members of the public to detect and mitigate. For example, law enforcement officials can create fake accounts and pose as members of the community they are seeking to gather information on. Officers in the New York Police Department have created accounts on Facebook, Instagram, and Twitter to gain access to information.

Digital Public Service Databases

Almost all public services in Canada have integrated the use of digital databases and organizational tools to collect information on individuals accessing these services. Welfare programs, homeless shelters, education and skills training, and supervised injection facilities are just a few examples of public services that solicit personal information from users. The Ontario Social Assistance Database (OSAD)[16] contains data about income support, benefit eligibility and amounts, and duration of social assistance, as well as characteristics of the client population. The Canadian Institute for Health Information collects data on a number of topics, from mental health and addiction to patient experiences. Data is collected on individuals who access public services for a number of reasons, such as to assess the impact the service has on the public, identify the most common reasons individuals access the service, understand the types of people who are most likely to use these services, and increase efficiency of service delivery.

All municipally-funded homeless shelters in Toronto are required to use the Shelter Management Information System (SMIS) database software. SMIS is a web-based software that logs when individuals enter and leave homeless shelters across the city. SMIS requires administrators to complete an intake form which requires information such as the individual’s name, date of birth, gender, age, family status, current sleeping arrangements, reason for service, and current source of income, as well as whether they have stayed at a shelter before.[17] However, shelters don’t require proof of an individual’s legal name, and it’s possible that multiple files could be created for the same person under multiple names. For this reason, data accuracy is a big concern. All information entered into the SMIS system is stored on City of Toronto servers, and meets the requirements of the Municipal Freedom of Information and Protection of Privacy Act (MFIPPA). Every individual who has had their data recorded through SMIS has the right to access their records. Data on shelter usage is available on the City of Toronto’s website. This data — in the form of a high-level summary — is updated daily based on the previous day’s data.[18] Data gathered through SMIS is an important tool to help inform research and policy on homelessness in Toronto.

The SMIS allows administrators to identify empty beds, manage shelter volume, and view daily occupation statistics. However, housing activists and advocates have accused the City of Toronto of misrepresenting shelter availability.[19] [20] Shelters in Toronto allocate beds based on age, gender, and family status. This means that only a certain number of beds are allocated to people within a specific category, such as single females or male youths. If a single female arrives as the shelter which has already filled all the beds for single females, she will be told there are no available beds – even if there are beds available in different categories.

This is part of a series of articles exploring personal data collection practices in Canada. Check out our previous article on Personal Finance, or move on to our next article exploring the data collection practices related to dating and genetic testing.

Technology and policy related to this topic are constantly evolving. If you think we have missed something or see an error please contact Sarah Villeneuve (sarah.villeneuve@ryerson.ca). If you want to get involved in subsequent phases of this project, apply here.


[1] SDM. “Rise of Surveillance Camera Installed Base Slows.” SDM, May 5, 2016. https://www.sdmmag.com/articles/92407-rise-of-surveillance-camera-installed-base-slows.

[2] Office of the Privacy Commissioner of Canada. “Guidelines for the Use of Video Surveillance of Public Places by Police and Law Enforcement Authorities.” Office of the Privacy Commissioner of Canada, March 2, 2006. https://www.priv.gc.ca/en/privacy-topics/surveillance/police-and-public-safety/vs_060301/.

[3] Brosnahan, Maureen. “Store Video Cameras Failing to Comply with Privacy Laws.” CBC News, December 28, 2012. https://www.cbc.ca/news/canada/store-video-cameras-failing-to-comply-with-privacy-laws-1.1189399.

[4] Office of the Privacy Commissioner of Canada. “Guidelines for the Use of Video Surveillance of Public Places by Police and Law Enforcement Authorities.” Office of the Privacy Commissioner of Canada, March 2, 2006. https://www.priv.gc.ca/en/privacy-topics/surveillance/police-and-public-safety/vs_060301/.

[5] Allen, Kate, and Wendy Gillis. “Toronto Police Have Been Using Facial Recognition Technology for More than a Year.” The Star, May 28, 2019. https://www.thestar.com/news/gta/2019/05/28/toronto-police-chief-releases-report-on-use-of-facial-recognition-technology.html.

[6] Lee-Shanok, Philip. “Privacy Advocates Sound Warning on Toronto Police Use of Facial Recognition Technology.” CBC, May 30, 2019. https://www.cbc.ca/news/canada/toronto/privacy-civil-rights-concern-about-toronto-police-use-of-facial-recognition-1.5156581.

[7] Conger, Kate, Richard Fausset, and Serge Kovaleski. “San Francisco Bans Facial Recognition Technology – The New York Times.” The New York Times, May 14, 2019. https://www.nytimes.com/2019/05/14/us/facial-recognition-ban-san-francisco.html.

[8] Thompson, Cadie. “Three Ways Social Media Is Tracking You.” Business Insider, May 28, 2015. https://www.businessinsider.com/three-ways-social-media-is-tracking-you-2015-5.

[9] “RCMP Tracked 89 Indigenous Activists Considered ‘Threats’ for Participating in Protests | National Post.” Accessed October 18, 2019. https://nationalpost.com/news/canada/rcmp-tracked-89-indigenous-activists-considered-threats-for-participating-in-protests.

[10] “RCMP Tracked 89 Indigenous Activists Considered ‘Threats’ for Participating in Protests | National Post.” Accessed October 18, 2019. https://nationalpost.com/news/canada/rcmp-tracked-89-indigenous-activists-considered-threats-for-participating-in-protests.

[11] “RCMP Tracked 89 Indigenous Activists Considered ‘Threats’ for Participating in Protests | National Post.” Accessed October 18, 2019. https://nationalpost.com/news/canada/rcmp-tracked-89-indigenous-activists-considered-threats-for-participating-in-protests.

[12] “RCMP Tracked 89 Indigenous Activists Considered ‘Threats’ for Participating in Protests | National Post.” Accessed October 18, 2019. https://nationalpost.com/news/canada/rcmp-tracked-89-indigenous-activists-considered-threats-for-participating-in-protests.

[13] “A Million People ‘check in’ at Standing Rock on Facebook to Support Dakota Pipeline Protesters | US News | The Guardian.” Accessed October 18, 2019. https://www.theguardian.com/us-news/2016/oct/31/north-dakota-access-pipeline-protest-mass-facebook-check-in.

[14] Waddell, Robinson Meyer, Kaveh. “Did the ‘Check-In at Standing Rock’ Campaign Start With Protesters?” The Atlantic, October 31, 2016. https://www.theatlantic.com/technology/archive/2016/10/facebook-is-overtaken-with-check-ins-to-standing-rock/505988/.

[15] “Police Use Surveillance Tool to Scan Social Media, A.C.L.U. Says – The New York Times.” Accessed October 18, 2019. https://www.nytimes.com/2016/10/12/technology/aclu-facebook-twitter-instagram-geofeedia.html.

[16] Government of Canada. 2015. “The Ontario Social Assistance Database (OSAD).” Statistics Canada. July 3, 2015. https://www.statcan.gc.ca/eng/rdc/data/surveys.

[17] “9926-SMIS_UM_HELP-Create-a-New-Intake.Pdf.” Accessed October 18, 2019. https://www.toronto.ca/wp-content/uploads/2017/11/9926-SMIS_UM_HELP-Create-a-New-Intake.pdf.

[18] Torontoist. “Civic Tech: Shelter Funding and Toronto’s Manufactured Crisis.” Torontoist, January 3, 2018. https://torontoist.com/2018/01/civic-tech-shelter-funding-torontos-manufactured-crisis/.

[19] Cole, Desmond. “NO Vacancy.” Cole’s Notes (blog), January 2, 2018. https://thatsatruestory.wordpress.com/2018/01/02/no-vacancy/.

[20] Torontoist. “Civic Tech: Shelter Funding and Toronto’s Manufactured Crisis.” Torontoist, January 3, 2018. https://torontoist.com/2018/01/civic-tech-shelter-funding-torontos-manufactured-crisis/.

For media enquiries, please contact Coralie D’Souza, Director of Communications, Events + Community Relations at the Brookfield Institute for Innovation + Entrepreneurship.

​Sarah Villeneuve
Policy Analyst
Stephanie Fielding
Policy & Research Analyst
January 21, 2020
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