Finding the ideal framework for smart city data sharing

Data sharing is an inevitability in smart cities and for city surveillance. While finding the "ideal" data sharing framework may be a challenge, with cooperation from all parties involved it can be done for the benefit of everyone.

Smart cities have been made possible because of the Internet of Things. This has resulted in an abundance of data, most of which goes unused. Implementing a strong data sharing framework for smart cities to share data between various entities (e.g., government agencies, businesses, residents, etc.) will allow cities to become smarter, safer and more efficient.

When it comes to city surveillance, data sharing has become an important aspect of ensuring that all relevant parties are provided with the necessary information. Privacy concerns and government regulations, however, can pose obstacles. 

By working with regulations and ensuring accountability, smart cities could implement a data sharing framework that benefits residents, businesses and the city as a whole.

The "ideal" framework

In smart cities, the ideal data sharing framework for video surveillance is built to serve both the smart city ecosystem and its stakeholders. These stakeholders range from the various government agencies and the entities operating within to the businesses, communities and residents of the city. The framework should allow each stakeholder to remain in command and control of their group’s data. At the same time, the framework needs to connect the various disparate systems that span these stakeholders.

"In a smart city a data sharing framework must have built-in, secure and traceable permissions that allow the owner(s) to share different silos of data, based on the agency or person accessing that data. Access to all shared data must be traceable for audit, with full transparency to all parties that 'touched' the data, when, and under what authority," said Giovanni Gaccione, Justice and Public Safety Practice Lead at Genetec.
 
It is important that every municipal entity has a policy across all departments with input and guidance from all departments (e.g., fire, traffic, police, water, etc.), advised Stuart Rawling, Director of Segment Marketing at Pelco by Schneider Electric. "These departments must have an aligned understanding of data sharing and legal implications both internally and externally. Best practices should start with a white list of data that's acceptable to share (such as basic physical appearance of crime suspects) rather than a black list of data that's unacceptable (such as a Social Security number). This way the default is to not share or leak data that might be subjectively deemed not sensitive, but later could prove to be just that," he said. 

on Grinfeld, Global Marketing Business Development for Enterprise Security at FLIR Systems, suggested an API-centric design that allows data integration not only with newly implemented digital/data-driven systems, but also with legacy systems. Furthermore, an ideal framework must also support large scale, high bandwidth, sensor-generated data streams, and allow both public and private data sharing. "The framework should be scalable beyond the traditional city-level users and allow support for new classes of value creators and stakeholders,” he added.

Intelligent security devices, according to Fan Yang, Vertical Solutions Manager at Hikvision Digital Technology, play an important role in smart cities in capturing key targets and information. These devices send the data to the backend to be analyzed and are stored in a video structured data pool. “This valuable data will be converged on-demand to different application systems of a smart city, such as city traffic, public security, city management, environmental governance, etc."

In terms of video surveillance, since video is converted to data and then further fused with algorithms, which are then applied in different businesses, Yang pointed out that this data could eventually help with intelligent operations, management and service delivery. "Therefore, an indispensable component of an ideal framework should include AI-powered intelligent devices to capture information, and a center for data analysis and storage, and an open application system for data sharing," he added.

Challenges in finding the "ideal"

Regulations at the federal, state and local levels can all pose challenges to implementing a data sharing framework —this includes the public concern for privacy. However, city surveillance by itself has few challenges when it comes to data sharing, as long as certain privacy and data protection protocols are observed, and the data was captured in public locations, according to Gaccione. This is because most, if not all, of what is recorded in cities are of public places.

"The privacy and regulations come in when one fuses that video data with PID (personally identifiable data), which elevates that video from publicly generic to a record about someone or something. Sharing video surveillance before PID is added allows for easier distribution and collaboration,” Gaccione added.

The GDPR (General Data Protection Regulation) generated a lot of press earlier this year regarding the way it is changing the landscape of data privacy and equality across Europe. Yet, the regulations contained within the regulations are actually not entirely new.

"Many countries in the European Union and around the world have had data protection and individual privacy regulations in place for decades. However, sometimes such regulations were effectively unenforceable across international boundaries — the GDPR has highlighted and cemented consequences for non-compliance across international boundaries. This has forced a global change in behavior even though the regulations only apply to citizens of the European Union,” explained Rawling. “One component of GDPR is referred to as ‘the right to be forgotten,’ which is essentially an individual’s right to request data about them be removed from applicable databases. To comply with this in a city surveillance environment, there needs to be policies in place to respond and research any requests and form a response based on the organization’s policy,” he continued.

 

The risks associated with the collection, sharing and misuse of data is also a major challenge — this includes legal risks as well as cybersecurity risks and other related breaches that could lead to data loss and data theft. “Additionally, competitive risks are a challenge, where competitive assets and insights could reside within the shared data and potentially fall into the wrong hands,” Grinfeld said. 

To overcome these challenges, Grinfeld recommends that the data sharing framework be built on top of an architecture that can fulfill the objectives of the ideal framework, addressing the needs for data control, sharing and cyber hardening. Additionally, he added that it is also critical to define best practices and common use cases for data sharing, adopt proper policies (and create new policies as needed), and develop action plans for data sharing implementations and collaborations between the various entities. “This will ensure that the essential areas are properly addressed while creating efficient execution cycles to bring the framework into reality,” he said.

Benefits and the future

One of the benefits of a data sharing framework would be improved situational awareness of real-time security and public safety operations, Grinfeld said. “In other words, using data consolidations to gain a better understanding of what is happening in real time, allowing for improved decision making and higher efficiency in handling security and public safety events.”

In the future, Grinfeld sees big data utilizations becoming a trend. Such utilizations would involve smart processing of consolidated data that could help detect and analyze various patterns, anomalies, trends and behaviors with direct impact on security and public safety objectives. "Various benefits can arise from such utilizations, starting from the ability to identify problematic use cases, moving on to increase operational efficiencies of government and municipal agencies operating across the city. Ultimately, cities would like the ability to predict the occurrence of security events prior to these events taking place, allowing to prevent them entirely in some cases and in other cases, achieve better outcomes. This is the future we are all working toward."

While the idea of "pooling or warehousing" of data is what many data sharing frameworks today revolve around, Gaccione believes that this goal is too much of a stretch for most organizations at this time. However, it could be part of our future. "Do I believe that someday we will have a 'data warehouse' or 'lake' per city? Sure, I do hope that, but the reality is that each stakeholder holds information differently. This has less to do about the technology and more about the use case," he said.

For city surveillance, Yang believes the continued advancements and development of AI technology, big data, and cloud computing will make data collection more precise and efficient in the future. “Effective data collecting will enable faster updates, which can be shared to various areas of smart city. Furthermore, it will also allow the whole system to self-adapt and -adjust in a more-timely manner.”

Frameworks for the future

Going forward, data sharing will be a huge part of future smart cities and city surveillance, and learning to properly harness its power will make all the difference. While there are limitations as to how much data is being shared and with whom it can be shared, with the proper framework the possibilities are endless.