Internet of Behaviours

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Contents

Background

Transformation of data from IoT to IoB

The concept of extracting and discovering behavioural patterns in large data sets was developed in 2012 by Gote Nyman at the University of Helsinki.[1] The field of data mining has evolved significantly in the past decade. A global research and advisory firm Gartner "predicts that by year-end 2025, over half of the world's population will be subject to at least one IoB [Internet of Behaviours] program, whether it be private, commercial or governmental".[2]

Internet of behaviours (IoB) is an extension of Internet of Things (IoT) where data collected through numerous technological devices are analyzed through a behavioural psychological lens, providing insight into things like consumer behaviour, their interests, and preferences. The main goal of IoB is to improve efficiency and quality of products and services offered. [3]

By 2023, it is expected that 40% of the global population will be tracked digitally using IoB. [4]

IoB combines and processes data from many sources including[5]:

  • Commercial customer data
  • Citizen data collected by the public-sector and government
  • Social media
  • Location data
  • Public domain deployments of facial recognition

It comprises of three main components which will be discussed in the later sections: behavioural science, and technology and data analytics from IoT. [6]

Behavioural Science

An important component of IOB is behavioural science, which is the study of human actions where you would observe and interpret behaviours then modify them afterwards.[7] Applications of behavioural science today include being used to understand patterns consumers make when purchasing goods or services, and also how to motivate and influence employee behaviour.

In a technology context, four main areas of behavioural science are covered[8]:

  • Decisions: Your choice to pursue a certain belief or course of action
  • Emotions: The state of feelings that can alter your behaviour
  • Augmentations: How you are influenced into thinking a different way than you normally would if there are other factors present that changes what your choice would typically be
  • Championships: Your support or defense for something

How IoT and IoB are related?

A British visionary named Kevin Ashton coined the term IoT in 1999 to describe a network of interrelated computing devices that collect and share data without human input. [9] The data is collected through embedded technologies such as sensors or processors.[10] As companies learn more about us through IoT, they can subsequently alter our behaviours (IoB). [11]

Internet of Things (IoT)

The term IoT is mainly used for devices that wouldn't usually be expected to have an internet connection. For instance, door locks were not conventionally expected to be connected to the internet; however, the wifi-enabled smart locks do, which is why they are considered an IoT device. IoT also includes any stand-alone devices connected to the internet that can be monitored or controlled remotely, like smart home technologies that include security cameras, smart thermostats, or kitchen appliances.

What sets an IoT apart from a regular device (e.g., a normal door lock) is the sensor, microprocessor, and it's connection to the internet. [12] A sensor is a device that detects changes in its environment and sends that information to the processor. The processor takes the signal sent by the sensor as an input and uses arithmetic logic to perform a function.

Type of Sensors

Sensors present in IoT devices are used to collect different kinds of data about users that is later analyzed from behavioral perspective. There are numerous kinds of sensors that are available nowadays. However, below is the condensed list of commonly found sensors in multiple IoT devices like smartwatches, smart thermostats, smartphones, etc. Since a smartphone is the most widely owned IoT device, it is used to illustrate the working of some of the sensors listed below.

Accelerometer

This sensor adjusts the screen when a smartphone is tilted. [13] Basically, it helps to determine whether the phone is in portrait or landscape orientation. In smartwatches, it is also used as a pedometer to count how many steps a person has taken based on which it counts how many calories they have burnt and the distance covered.[14]

Other applications include:

  • In vehicles, aircraft, and missiles to measure vibration, shock, and electrical capacitance.[15]
  • In industrial machinery subjected to constant high stress like pumps, compressors, and turbines to detect faults.[16]
  • In biological sciences to study the behavioral patterns of animals, which is useful in the preservation of endangered species.[17]
  • In remote monitoring of volcanic activity.[18]

Even though accelerometers are generally considered non-intrusive, some studies have found that "third-party apps [on smartphones] can access accelerometer data without requiring security permission".[19] "Accelerometer data alone may be sufficient to obtain information about a device holder's location, activities, health condition, body features, gender, age, personality traits, and emotional state".[20]

Ambient Light Sensor

Light sensor in iPhone5

When a smartphone is set to auto-brightness mode, this sensor detects the light or brightness in the external environment to automatically adjust the brightness of the display.[21]

Other applications include:

  • In LCD and LED displays to adjust the brightness according to external light.[22]
  • In agricultural sprinklers to automatically water crops when the sun is not at its brightest.[23]
  • In public buildings and washrooms to turn on the lights when it is occupied.[24]

Light sensors can detect even the slightest changes in the external environment of the device, which gives rise to various privacy concerns. According to a researcher at University College London, data from these sensors can be used to determine the type of lighting a person prefers, the size of their home, a map of their home arrangement, the time of the day they work, and how frequently they move around the house or leave altogether.[25] Such information can be used to build a person's profile, assigning them "to a particular category, such as this user has a large house, he is wealthy. Why not target web content based on this information?”[26]

Biometric Sensor

Iris Recognition

Biometric sensors such as Fingerprints and Face Detection sensors are commonly found in smartphones.

Iris Recognition is another type of biometric sensor that is used by authorities in identifying individuals based on unique patterns in their iris (the colored ring-shaped tissue in the eye that contains the pupil). This technology is employed by CBSA (Canada Border Services Agency) in NEXUS lanes at some locations.[27] It is also used in law enforcement to compare iris images of suspects through an existing image database to confirm their identity. Iris scans are quicker and more reliable than fingerprints because it is easier for an individual to obscure or alter their fingers than it is to alter their eyes.[28] Furthermore, it is possible to scan the iris from a distance or even when the subject is moving, which raises civil liberties and privacy concerns.[29]

Barcode and QR Sensors

QR code scanning

Almost all smartphones have sensors that can read barcodes and QR codes on product packaging either through an in-built camera or a third-party app. They also have a common application in devices used by the retail and manufacturing sector due to the nature of their business.

QR codes are also widely used in phishing attacks. They are an ideal attack method for cybercriminals because the human eye cannot differentiate a malicious QR code from a legitimate one.

In 2015, Heinz (the ketchup company) ran a promotional campaign making use of a QR code to direct users to a site where they could design their own label for a ketchup bottle.[30] However, once scanned the QR code directed users to an inappropriate site instead.[31] It turned out Heinz did not renew its registration for the domain name, and some other party was able to take their site domain before Heinz caught on.[32]

Gyroscope

Gyroscope in gaming remote

A gyroscope is another motion control sensor that can precisely detect the orientation, direction or rotation of the phone.[33] Many popular gaming apps such as Asphalt 5, Real Racing, and PUBG use the motion detection features of the gyroscope.[34]

It can also be found in the rotor of a helicopter, cruise ships, gaming consoles (like PlayStation controller or Wii remote), and virtual reality sets such as Oculus Rift which has now been discontinued by Facebook.[35][36][37][38]

Contrary to what is popularly believed, Gyroscopes are used instead of microphones on our devices to potentially eavesdrop on our conversations.[39] It works just well enough to pick up a fraction of the words spoken near a phone, gaming console, or other IoT device to determine the context of the conversation.[40]

Barometer

Premium smartphones such as Samsung’s Galaxy S21 series, Note 20 series or iPhone 12 have a Barometer.[41] This sensor measures air pressure which is useful in studying altitude change and weather conditions like storms.[42] The logic behind it is that decreasing atmospheric pressure predicts stormy weather.[43] The reason that phone manufacturers include barometers in the devices is to improve the functioning of GPS which could be adversely affected by atmospheric pressure.[44]

Like an ambient light sensor, Barometer can be very useful in combination with GPS to determine the device owner's locational data in terms of the altitude of the place they’re living and the prevailing atmospheric conditions there.[45] Also, since the readings subtly shift with increased altitude, it could give away which floor of a building they’re on if one is to track them.[46]

GPS Sensor

Global Positioning System

Global positioning system sensors are very common in smartphones nowadays. The maps on smartphones make use of this sensor.

It is also widely used in industries like marine, aviation, mining, and defense.[47] One of the interesting applications of GPS is in timekeeping. Smartphones derive their time from GPS signals but the GPS time is not precisely correct.[48] To display the correct UTC time, our smartphones make a leap second adjustment to GPS time.[49]

Google uses GPS sensors on smartphones to determine traffic congestion on different routes which users find concerning because it implies that their location is constantly tracked.[50]

Hall and Proximity Sensors

These are two different sensors, but they function in a very similar manner. Hall Sensors in smartphones can detect the magnetic field of flip phone covers to sleep and wake the phone as the cover is flipped.[51] A proximity sensor detects the distance of the phone from the skin or any other object.[52] A proximity sensor disables the touchscreen when one receives a phone call or holds their phone near to their ear.[53]

Other applications of hall sensor include[54]:

  • In automobiles to measure the level of fuel in the fuel tank or to determine whether the driver and passengers have put on the seat belt
  • In automatic flush, sinks, hand dryers, and elevators

Other applications of proximity sensor include:

  • In automobiles to alert the driver of any obstacles while parking[55]
  • In aircraft to alert pilots of nearby obstructions while taking off, flying, or landing[56]

As such, these sensors are weak on their own, and data collected through them may seem insignificant unless combined with data from other sensors to make relations.[57] However, if speculations are to be believed, proximity and hall sensors can sense other devices in their vicinity and interact with them to collect and transmit data. It means a machine is interacting with another machine without human input. It sounds dangerous, but more research is required in this field to fully understand its implications.

Magnetometer

Compass needle moves in response to earth's magnetic field

Magnetometer operates on earth’s magnetic field and detects the direction of the North Pole.[58] The compass in smartphones makes use of a Magnetometer.

Other applications include:

  • In the detection of archaeological sites, shipwrecks, other buried or submerged objects[59]
  • In finding the direction of drilling for oil and gas[60]
  • In defense forces to detect submarine activity to guard borders[61]
  • In the detection of iron ores like magnetite and hematite[62]

Just like Hall and Proximity sensors, it is speculated that a technically sophisticated magnetometer can also help two devices in proximity to pair up and extract information from each other like fingerprints or other biometric data. However, more research is required to verify the credibility of these theories.

Thermometer

Nowadays, smartphones also have a thermometer that monitors the temperature of the device and the battery.[63] This sensor instructs the processor to shut the device in case of extreme overheating to prevent damage.

Traditionally, thermometers are used for medical purposes like ear, forehead, and oral thermometers. In restaurants, thermometers are used to monitor food safety that is keeping food at optimal temperatures to stop potentially harmful bacteria from growing. Meteorological departments use it for measuring environmental temperature. It is also present in thermostats to regulate indoor temperatures.

In terms of privacy concerns, many smartphone users noticed that after they tracked their body temperature through an app, they started seeing ads for different ibuprofen brands.

Touch Screen

A touchscreen Sensor is used to input data into the phone. It can be found in all kinds of touch screen devices.

Covid-19 Implications

The onset of the Covid-19 pandemic may have accelerated the adoption of IOB technologies. [64] In this new environment, individuals are more accepting of being surveilled and tracked because the perceived public good trump's their private interests. The following example illustrates how citizens were willing to have their information tracked in order to prevent the spread of Covid-19.

Taiwan's Electronic Fence System

Airline worker providing information on Taiwan's quarantine system

This system was implemented at the beginning of the Covid-19 pandemic in early 2020 and was successful in limiting the spread of the disease in Taiwan. Travelers had to fill out an online health form and were required to quarantine for 2 weeks at a designated location. An individual’s cell phone location data was used to determine whether they were complying with their quarantine and action is taken if an individual breaks their quarantine. [65]

The successful adoption of this system can be contributed to 3 factors[66]:

  • Taiwan has a relatively small population compared to other countries (24 million)
  • 99% of the Taiwanese population own a cellphone which made it possible to use cellular tracking to enforce quarantine
  • Citizens believed this system was necessary to combat the virus for the greater good of the country

Their acceptance for this surveillance program can also been derived from citizens proactively wearing face masks even before they were required to do so. [67] This shows that they have a collectivist way of thinking that benefits the society at large, even though a majority of citizens in general are wary about their privacy.

In October 2020, it was noted that Taiwan only had 553 confirmed cases and 7 deaths compared to other countries who had tens of thousands of active cases. [68]

Other Covid-19 Applications

Additional applications of IoB to aid in the Covid-19 pandemic include:

Contract Tracing Apps

These applications on your smartphone or other smart device make a log of whom you’ve come into contact with and provide alerts to individuals who may have been exposed to Covid-19. [69] It uses your phone's Bluetooth signal to detect when two of the app's users are within two metres of each other. [70]


Social distancing wearables in action

RFID Technologies in Hospitals

Hosptials across the world are using RFID technologies to help with hand hygiene and to monitor social distance compliance during the Covid-19 pandemic. Nurses are given a RFID enabled lanyard to wear around the building, and these lanyards would track staff members based on their proximity to specific locations such as sinks, soap dispensers, and doors. A report is generated based on the information collected and can be used to discipline or reward employees.[71]

Previous studies have suggested that RFID technologies can help produce short- term improvements in hand hygiene, but it is unclear whether it can foster long-term changes in behaviour. [72]

Wearables

Wearable technology is given to employees and can either be worn on their wrists or belts. They consist of sensors that connect via Bluetooth or GPS to other devices which will create noise or vibrate to notify staff that there is a person within six feet of them. [73]

IoB in Business

IoB is a key component that organizations need to embrace in becoming a smart, data driven business as it allows companies a 360 degree view of their customer’s mindsets.

The experience that companies bring to customers is just as important as the quality of the product or service provided. According to a study conducted by PWC, one bad experience by a brand is enough to drift away 32% of their customers.[74] The effects and uses of IoB in a business setting will be discussed in this section.

Business Benefits

Some of the benefits that IoB creates for businesses include:

1. Marketing products more effectively to consumers

IoB allows businesses to generate new insights into their customer’s buying habits across platforms, as well as how their customers interact between their devices and products/services. This can make it easier for them to predict and understand trends and patterns which will ultimately increase revenue. The technology stemming from the interconnectivity of IoT also allows businesses to send real-time, targeted advertisements to their customers through multiple devices and means. [75]

2. Improves consumer experience

Understanding the consumer’s purchase journey will allow businesses to create a more seamless and personal experience that will entice consumers to continue engaging with a brand. Businesses will also be able to resolve customer issues quicker. [76]

3. Helps with operational efficiency IoB can be used to improve internal operations of businesses by correcting or improving the behaviour of employees through devices. [77] Data collected through IoB can also be used to replace traditional methods of data collection such as customer surveys which are time consuming, helping to improve business strategies and create new products and ideas. [78]

Cohort Analysis

Examples of cohort analysis

Cohort Analysis is a component of IoB in business as it is used to classify consumers into different target market groups. Cohort analysis in IoB is a behavioral analytics tools that takes data from multiple sources, such as sensors, and analyzes it. These groups, or cohorts, usually share the same characteristics. In cohort analysis, businesses can see behavioral patterns that can be used to generate insights. For example, cohort analysis can help measure user engagement with business over time and help businesses adjust to improve engagement rates, such a business adjusting their marketing strategy or adding certain features to the product.

One of the most common cohorts that businesses use is acquisition and behavioral cohorts. Acquisition cohorts track the number of users that use the product over a certain period of time. For example, cohorts can be based on the amount of customers using the product every day. This type of analytics can help businesses analyze the retention rate and make suitable changes to increase it. Behavioral cohorts divide users by the certain actions that users perform using the product. For example, behavioral cohorts can help track users who did a transaction or users who used a certain feature of the product. [79]

Examples of cohort analysis tools include Google Analytics, Facebook Insights, Mixpanel, Amplitude and Kissmetrics.

Applications in Business

“IoB technology is going to collect the digital footprint of users and use that knowledge for the benefit of business and consumers.” [80]

Below are some examples of IoB in a business setting.

Vitality Health App

Marketing: Vitality Health

Vitality Health is a health insurance company that utilizes data and technology to track and reward healthy behaviour. [81]

An initial health assessment is collected for each member, and the app will monitor whether the member is making progress towards their health goals through wearable technologies like the Apple Watch. Data collected from wearables are also combined with data from Vitality’s partners and network gyms. They have a rewards point system which rewards members for engaging in healthy behaviour, and includes items such as gift certificates, discounts on healthcare plans, and other perks from their partners.

RAND Europe, a research institute concluded that incentivizing physical activity does lead to a positive change and maintenance of good behaviour. [82] One of Vitality Health’s partners had a 92% increase in engaged participation levels from customers. [83]

Operational: Amazon’s Delivery Drivers

Amazon has installed AI-powered cameras in their delivery vans, and will be used to track driver behaviour. This technology provides drivers with real-time feedback regarding their driving behaviour while also collecting the data that will be used to evaluate their drivers.[84] Amazon hopes that this technology will not only improve the behaviour of their drivers, but also keep the community safe.

Information tracked include[85]:

  • Vehicle location and movements: This includes things such as miles driven, speed, acceleration, braking, and following distance from the car in front of it
  • Potential traffic violations: This includes things like speeding, failure to stop at stop signs, undone seatbelts
  • Risky driver behaviour: This includes distracted or drowsy driving

Business Implications

IoT and IoB “itself isn’t inherently problematic; a lot of people like having their devices synced and get benefits and convenience from this setup. Instead, the concern is how we gather, navigate, and use the data, particularly at scale.”[86]

With IoB, businesses gain access to a multitude of data and other analytics. But with great knowledge comes great responsibility, and they need to ensure not to overstep boundaries and exploit consumers.

It is difficult to predict whether IoB will be able to detect harmful behaviours in individuals such as untreated addictions like shopping addictions as well as compulsions and prevent a further decline in their well being.[87]

IoB in Gaming

IoB is widely used in gaming as a tool to analyze the data collected from the user and optimize the gaming process. Understanding the behavior of the players helps game studios understand how to make the game as much addictive and entertaining as possible in order to maximize profits from the monetization of the active players.

Commerical Benefits

Game production companies typically analyze various metrics to generate insights about the game and make it addictive. Some of the most generic metrics include daily active users, percent of users acquired daily, engagement score and many more.[88] Analyzing these metrics can help companies to benefit in two ways:

1. Increased conversion rates

IoB can help gaming companies understand how to convert customers to paying users. Companies can analyze the data they collect from users to get insights on what makes the customers pay the most amount of money in the long run and adjust their game structure and pricing strategy based on this information.

2. Increased retention rates

IoB is a tool that helps measure and increase the engagement of the players over the period of days, weeks, or another time period. [89] Game production companies aim to generate more profits and IoB helps to understand how to make users play as much as possible and create a long-term engagement. Analyzing gamer tendencies and preferences that these companies collect using the IoB helps them create the most optimal user paths that the player will repetitively follow.

Fun in Gaming

Dark Souls - a popular game series that combines fantasy, narrative, challenge and discovery types of fun

People love playing games and there are many reasons for it. The biggest and the most obvious one - games are fun and people like having fun. According to Marc LeBlanc, a game designer from MIT, there are 8 types of fun found in different genres of games: [90]

1. Sensation: Game as sense-pleasure. Games that evoke emotion in the player, be it through sound, visuals, controller rumble or physical effort.

2. Fantasy: Game as make-believe. Game as a means to take the player to another world. Some call it escapism.

3. Narrative: Game as drama. Game as a means to tell a story or narrative to the player.

4. Challenge: Game as obstacle course. Games that provide the player(s) with highly competitive value or with increasingly difficult challenges.

5. Fellowship: Game as social framework. Games that have social interactions as its core or as a big feature.

6. Discovery: Game as uncharted territory. Games in which the player explores a world.

7. Expression: Game as self-discovery. Games that allow for self-expression from the player through gameplay.

8. Submission: Game as pastime. Games that have "farming" or "grinding" as a core element.

Game production companies use multiple types of fun in their games to diversify the experience for the players and prevent them from being bored. From the profitability standpoint, Some types of fun are more sustainable than others. For example, narrative games are most commonly finite stories that have less opportunities to keep the player in the long run. Fellowship, on the other hand, has fewer limitations as the opportunities to have fun expand beyond the virtual world.

Game Monetization Models

Game production companies spend a lot of money on producing games that will be interesting for a larger portion of a gaming population in the context of the high standards for game quality. Some companies spend over $100 million dollars and years in developing. For example, the most expensive game in history, GTA 5 cost Rockstar Games $265 million dollars. [91] Hence, companies need to ensure that the game will be profitable. There are four different ways to generate money from games:

1. Single Payment - The most conventional way to pay for the game. A customer pays a fixed price and gets full access to the game. Once the customer pays - the game production company has reached all of its potential to generate money from the game.

2. DLC - the downloadable content or certain extensions to the existing game that can be bought for a certain price.

3. Subscriptions - users get full access to the game by paying a certain amount per month.

4. Freemium - users get full access to the gameplay and can get additional features for a fee. More information on the freemium model can be found below.

Fortnite - an example of a successful freemium implementation

Freemium

Freemium is payment model that has gained popularity in recent years. The game is free to play but the user can pay to get access to additional features of the game. These features can have no effect on the gameplay - for example, cosmetic items in a competitive game. However, sometimes these features can have a direct impact on the gameplay and gives you an in-game advantage if you compete with free-to-play players (players who choose not to spend money on the game). For example, it could be buying additional weapons in a shooter game.

Freemium has an advantage in terms of attracting new customers to a game as it removes the biggest barrier from the game - the initial payment barrier. It is also the most efficient when generating revenue as it targets multiple customer groups that are paying money based on their spending habits. There are three distinguished customer groups: [92]

1. Whales - Whales are the top 1% of the gamers that spend the most and typically generate about 58% of the in-game revenue.

2. Dolphins - Dolphins are the customers that spend money on the game approximately once a month and are considered to be medium spenders.

3. Minnows - Minnows are the customers that spend money on the game occasionally, typically during big in-game events.

Implementing IoB in gaming can help gaming companies utilize the right payment model in the right and generate the maximum potential profit. For example, Fortnite proved to be a successful freemium game, where 69% of the customers spent money on in-game purchases. [93] This is well above the average conversion rate of 3.4% for the PC games. [94]

Gaming Addiction

If companies are too successful at making their game addictive, it might create social issues such as increased rates of gaming addiction, which was recognized as an official disorder by the WHO. [95] It will be difficult to prevent this as it will be nearly impossible to regulate the game production companies. However, from the ethical standpoint, companies that use IoB in gaming can use predictive analytics to identify customers who are susceptible to gambling addiction and gaming disorders. [96] They can potentially use this information to help susceptible players and prevent them from getting addicted.

IoB in Security

IoB technology can also be used in security across many different types of organizations. The use of behavioural analytics and IOT helps detect compromised information by finding unusual activity, and is employed by both government agencies and private companies throughout the world.

User and Entity Behaviour Analytics (UEBA)

An insider threat detected by Exabeam's Fusion SIEM.

User and entity behaviour analytics is an advancement of user behaviour analytics to monitor the behaviours of users and other entities such as routers, servers, and endpoints. It's primary goal is to track for potential insider threats among the systems within an organization.[97]

UEBA is a type of cyber security process that takes note of the normal conduct of users which is then used to detect unusual behaviours. UEBA uses machine learning, algorithms, and statistical analyses to know when there is a deviation from established patterns, showing which of these anomalies could result in a potential, real threat. UEBA also has the ability to monitor cloud-based assets to determine if they are acting normally. The use and advancements of UEBA have been boosted by the boom of IoT technology. [98]

Exabeam

An example of UEBA is Exabeam’s Security Intelligence Platform. Exabeam is a full Security information and event management (SIEM) solution based on modern data lake technology with UEBA capabilities called Fusion SIEM. The addition of UEBA to Exabeam's SIEM solution can detect security incidents that traditional tools do not see, because they do not conform to predefined correlation rules or attack patterns, or because they span multiple organizational systems and data sources. Exabeam can also be deployed as a cloud-based service. [99]

Exabeam detects insider threats, or potential security risks that are made within an organization's system. For example, disgruntled employees who have access to the system could potentially engage in malicious behaviour that Exabeam's Fusion SIEM would detect.

A list of Exabeam's UEBA capabilities include[100]:

  • Rule and signature-free incident detection
  • Automatic timelines for security incidents
  • Dynamic peer groupings
  • Lateral movement detection

Government Applications using IoT

Currently, governments use IoT Technology to track data, gather intelligence, apprehend criminals, and many different types of applications. These are the building blocks for potential future applications of IoB technology among governments.

SignalFrame

SignalFrame, a Washington, D.C.-based wireless technology company, has developed the capability to tap software embedded on as many as five million cellphones to determine the real-world location and identity of more than half a billion devices. SignalFrame’s technology can turn any smartphone into a listening device, also called sniffers, that detect wireless signals from any device that happens to be nearby. As of 2020, the U.S. Air Force’s research arm has awarded a $50,000 grant to SignalFrame as part of a research and development program to explore whether the technology has potential military applications. SignalFrame has used this technology previously with American Communications company, AT&T, to measure the adoption of their new wi-fi routers. [101]

China's Social Credit System (SoCS)

China’s social credit system is a set of databases and initiatives that monitor and assess the trustworthiness of individuals, companies and government entities. Each entry is given a social credit score. Based on these scores, there are according rewards for those who score high and punishments for those who score low. Data about each entry is usually collected from financial, criminal, and government records.[102] The SoCS is primarily focused on monitoring companies to ensure compliance with the law.[103]

A future goal of the SoCS is to "use big data to modernize national governance".[104] Therefore, it is likely that the Chinese government will eventually incorporate IoB technology into the SoCS in the future.

Ethical Concerns

IOB could potentially be asking us to change our cultural norms and behaviours based on the agendas of larger bodies who feed us content. If the agendas of these larger bodies, such as governments or corporations, are malicious, the use of IoB technology could be devastating. Moreover, giving large corporations the potential power to influence our behaviour seems problematic.

Issues with Privacy Laws

Much of the scope and execution of an IoB will depend on local privacy laws, which may affect how data can be used and in what way. Current privacy laws provide little legal protection against the collecting and use of big data. These laws also vary from jurisdiction to jurisdiction making it harder to mandate across all legal bodies and countries alike. Moreover, the process needed to update these laws to keep up with current technology is very time-consuming. This presents significant legal and security risks to privacy rights. For example in the United States, there is no one law that mandates data collection. Rather, it is a combination of laws and regulations regarding telecommunications, health information, credit information, financial institutions and marketing.[105]

Changes to Canadian Privacy Laws due to COVID-19

Several provinces across Canada, including British Columbia and Ontario, have changed their data collection and privacy laws to respond to the urgent need for government agencies to collect data to develop effective strategies and responses to COVID-19.

  • On March 25, 2020, the Ontario Government made amendments to Bill 188 to the provincial Personal Health Information Protection Act (PHIPPA) to introduce a new entity, "consumer electronic service providers". Consumer electronic service providers include mobile device app developers, telecommunications service providers, and online portals that process personal health information. [106]
  • On March 17, 2020, The British Columbia Government introduced a new ministerial order to allow the broader use of digital technologies under the provincial Freedom of Information and Protection of Privacy Act (FIPPA) to accommodate for post-secondary institutions and front-line healthcare workers. [107]

The following section will address ethical concerns regarding smart home devices.

Smart Home Devices

Google Home Device: The Google Nest Mini

A smart home device can be automatically controlled remotely from any mobile or network-connected device via the Internet.[108] Smart home devices, such as the Amazon Alexa[109] and Google Home[110], have built in environmental sensors which collect information about the user and their behaviour. Powerful analytics can be used to make use of this data to learn about a user's wellness and health-related insights.[111]There have been many arising concerns among smart home product users concerning privacy.

Smart Home Speakers and Data Collection Issues

Researchers at Northeastern University have discovered that smart speakers often record audio despite not being summoned by the devices' wake words. Wake words are words or phrases used to trigger smart home devices into listening and recording audio. For example, Google Home's default wake words are "Hey, Google".[112] During the study, researchers put on numerous types of shows and movies to test the devices to see if they were triggered into recording dialogue. In total, they had uncovered that the devices had picked up on 134 hours of listening with one "false positive" wake per hour.[113] However, after running the trial again, it seemed that the devices were not triggered by the same words as often which could possibly be a result of their AI learning ability.[114]

Google

Google's 2020 Privacy Lawsuit

In 2020, Google was involved in a 5-million dollar class-action lawsuit in which they allegedly collected data from chrome users when they were on incognito mode.[115] Google responded by admitting that tracking is possible in incognito mode but only by the websites themselves and not Google.[116]

Selling of Collected Data

Google monetizes the data it collects by using real-time bidding. Real-time bidding is when publishers auction off ad space in their apps or websites while sharing sensitive user data such as geolocation, device IDs, cookies, and browsing history.[117] Real-time bidding includes:[118]

  • The collection of data to build individual profiles (including information about demographics, interests, patterns, and behaviours), to allow advertisers to target groups.
  • The sharing and bidding of data directly with advertisers.

Cybersecurity

With everything now being interconnected with IOT and IOB technology, hackers now have more access to personal information. The possibilities are endless and could include: property access codes, delivery routes, and even bank access codes. Since IOB allows us to collect and track data about a user’s behaviour, hackers could generate more advanced scams, tailored to the habits of individual users, which would therefore increase the likelihood that users will be scammed.[119] Therefore, phishing and fraud could become more detrimental and personal information will become more at risk if proper measures are not taken to ensure that all this data is being kept secure.

Risk Mitigation

Organizations must be able to protect and ensure that all of the valuable information collected through IoB technology is secure to ensure that its use does not become catastrophic. Some methods of data security risk mitigation include:

  • Privacy rights of the consumer are of utmost importance and should be at the forefront of the data collection and security process.
  • Any organization that uses IOB tech should create a secure platform for storage and execution of data. Some methods for creating a secure platform include[120]:
    • End-to-End (E2E) Encryption[121]
    • Confidential Computing[122]
    • Software-defined perimeter (SDP)[123]
  • Consistent revision of privacy laws and regulations from government bodies
  • Organization-led cyber security education[124]
  • Ensure that users are consenting to data collection

Future Predictions for IoB

The future of the Internet of Behaviours could lead to many different outcomes. Our team's predictions for the future can be categorized into positive and negative outcomes. The team believes that either of these outcomes are equally possible.

Potential Positive Predictions

The emerging use of IoB technology can help benefit us in many different ways. Some potential examples include:

  • Consumerism having the potential to become a more personalized and tailored experience. For example, businesses could improve consumer offerings and suggestions
  • Improved consumer targeting and marketing tactics tailored to match consumer behaviour
  • Learning-type gaming applications which adapt to user behaviour could be created to personalize learning experiences and could have potential educational uses.
  • The possibility of a sophisticated and advanced healthcare system whereby wearables are able to diagnose and run health check-ups on users based on their behaviours, health patterns, and consumer patterns.
  • Governments using IoB technology to discourage or encourage certain behaviours. For example, wearables being able to alert authorities when a user is texting and driving.

Potential Negative Predictions

Although IoB has numerous benefits, the abuse of this technology could lead to catastrophes. Some potential examples include:

  • Increased addiction rates (i.e. shopping, gaming) due to companies being able to influence consumer behavior
  • Increased government surveillance and abuse of government power to influence people to adhere to problematic agendas.
  • Increased risk of security threats, as more data creates more temptation to steal and misuse it
  • Increased data collection by large companies, which they can use to their advantage with little to no regard to the overall well-being of the society

Summary

Though it is unclear of which way IoB will go in the future, our group feels that it is important that citizens are aware and educate themselves about the privacy and ethical concerns surrounding this technology, and be their own advocates.

As IoB technologies grow more prominent, there will be more ambiguity in terms of how the data collected from citizens will be used by entities like the government, businesses, and other external entities. The issue at large is that existing legislation has difficulties in keeping up with regulating new outcomes and methodologies that technology introduces. Thus, it is difficult to rely on governments to be able to protect us from harm. Without understanding the implications and holding organizations accountable, IoB will shift towards a dystopia where citizens will be exploited, and privacy will be further compromised. However, they also have the chance to turn IoB into a positive aspect that can benefit their lives by making sure they understand how their data is being used and what is acceptable for organizations to use their data for.

Authors

Lovepreet Kaur Annie Zhou Taylor Vaz Temirlan Kakimov
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada
Beedie School of Business
Simon Fraser University
Burnaby, BC, Canada

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