Have You Ever Wondered What Happens To The Massive Amounts Of Data Collected By IoT Devices? How Would You Feel If This Data Was Used To Influence Your Behavior?

Internet of Things – An Overview

Internet of Behaviors, To be a regular user of Internet of Things (IoT) devices, you don’t need to be interested in gadgets or even an early adopter or know what the Internet of Things is. The number of smart devices we use daily is constantly increasing. Internet doorbells, Internet-connected cars, smart speakers, security systems, and fitness trackers are just a few examples of IoT devices that are considered mainstream today and are regularly used in our daily lives.

Internet of Things technology does not only refer to simple consumer products but is a massive trend in industry and business. Inventory tracking, logistics management, production line sensors, security, and even sentiment analysis or opinion mining through intelligent algorithms are examples of this technology’s applications that show how the Internet of Things revolution simplifies processes and causes changes in different sectors.

In general, the Internet of Things has brought about many life changes, the most important of which are the perceived benefit of fulfilling promises and the generation of large amounts of data.

Perceived benefit as promised

For example, suppose you will spend 14 million Tomans on a juicer or a toothbrush that connects to the Internet. In that case, you expect your IoT devices to provide proper functionality with a perceived benefit.

A fitness machine that turns your activity into a game and encourages you to stand or walk is a good thing that improves life. But you can say good night to your smart speaker, and with this, the lights inside the room are turned off, and the motion detection option on the outside cameras is activated; for many, it brings peace and comfort to Armaghan. Similar benefits are being realized in the corporate space, albeit on a different scale.

Generating large amounts of data

In addition to selling a great new device that does a seemingly valuable job, IoT product vendors also generate and record vast amounts of data related to your interactions with these devices. According to Cisco’s forecast, IoT devices will have generated more than 800 zettabytes of data per year by the end of 2021, and this amount will grow exponentially in the following years. A zettabyte is equivalent to approximately one trillion gigabytes.

This massive amount of data can provide actionable insights into our collective and shared behavior in specific situations if adequately processed. These insights are precious to companies looking to improve their product development and marketing strategies. By collecting data and sifting through helpful information, companies can make accurate predictions of what humans will do when faced with specific situations.

What is the Internet of Behavior (IoB)?

Professor Göte Nyman from the University of Helsinki first explored the concept of the Internet of Behavior (IoB) in his blog in 2012. In his first paper, which examined the patterns of behavior behind the Internet of Things, he wrote: “I believe that shortly we will see an explosive growth of applications and services that, to provide the best possible answers, access to data, communication, information, Interaction, entertainment, service, and performance rely on receiving guidance and information directly from individuals and communities.

The idea of the Internet of Behavior focuses on human activity through the lens of behavioral psychology. Using big data to understand how we will behave in certain situations is helpful for large organizations (including large corporations and organizations) worldwide. It is essential to know that Professor Nyman introduced these concepts long before the Internet of Things revolution took off, and many engines were created to gather information.

In the book Top Strategic Predictions for 2020 and Beyond, Gartner mentions the Internet of Behavior and talks about the concept of Hyperpersonalization, which is based on the continuous collection of data to identify consumer sentiments and use this knowledge to increase sales.

According to projections, by 2023, people’s activities will be digitally tracked by the Internet of Behavior to determine benefits and eligibility for services for various people worldwide, primarily in the United States.

Gartner says this: “With the increase of technologies that collect the digital dust of everyday life, that is, data that includes both the digital and physical worlds, this information can be used through feedback loops to change behaviors.”

However, whether we like it or not, the Internet of Behaviors has access to data collected from algorithms and billions of IoT devices. Large tech companies and organizations will use this data to improve the user experience for their benefit.

Companies that know us through data obtained from smart devices can now influence our behavior using IoB analysis of this data, but this influence is not always a bad thing. For example, consider a health app or wristband that you use to track your eating and sleeping habits and monitor your heart rate or blood sugar levels. This program or device can warn you about a dangerous health condition and suggest changes in your behavior and lifestyle that will positively affect you. If you ask Apple, it will tell you many stories about how its smartwatch has saved users’ lives.

How does the behavior of the internet work?

The concept of the Internet of Behavior can be considered in two high-level steps as follows:

• Measure, collect and understand: The continuous use of big data from multiple sources (by no means limited to the Internet of Things) to measure and understand the collective behavior of people in specific situations.
• Influencing and guiding behavior: The collected views can be used and developed to guide people’s behavior in certain situations. Its most common application is in a commercial (usually retail) environment. Still, on a broader scale, its global examples are used by large organizations and governments through hyperconnectivity and access to multiple data points.

Apparent security and privacy concerns

While this level of insight into people’s behavior is seen by many as a positive agent for change (especially in marketing, business, and social media), there are many concerns about how this data is collected and used. Issues of privacy and ethics are always at the fore in all discussions of data collection, sentiment, and sentiment analysis.

Why should we trust big retailers, social media companies, and organizations with so much information about us? Big companies like Facebook have misused user data to shape public opinion, spread fake news, or sell this information to third parties. Cambridge Analytica is just one of these cases. Also, large-scale cyber attacks and data breaches are widespread occurrences; Hence, there is a possibility that this data will fall into the wrong hands. Is this just an invasion of our privacy on a personal level, or are we implementing a global social validation system?

Address privacy concerns

However, there is a computational process called homomorphic encryption that allows calculations to be performed on encrypted data without initial decryption.

This critical technology allows organizations to securely outsource data to third parties specializing in analyzing and processing big data while the data is encrypted. This can save money, as companies can securely provide customer data to third parties for analysis without developing ample in-house data expertise.

Also, customers and partners will be assured that the data collected is encrypted and anonymous, which increases trust and brand loyalty.

Whether this information is 100% anonymous or not, the debate about ethics and the use of large-scale behavioral data obtained from various sources and expressing the actual views of people in specific situations is not an issue that can be easily ignored.

The biggest obstacle to the large-scale adoption of homomorphic encryption is its slowness. This technology is slow enough that it is not yet practical for many applications. However, some companies and researchers are trying to speed up this process by reducing the computational overhead required for homomorphic encryption.

Organizations need to be more transparent when their customer data is anonymized and encrypted. If this becomes a norm, customers and even employees of that organization will get detailed information about the strategies of that organization. This clarification should be visible to people, not be mentioned in the form of one or more paragraphs, and few people pay attention to it.

Why is homomorphic coding revolutionary?

The problem with encrypted data is that it must first be decrypted to work with it. By doing this decryption, you make the data vulnerable to the very things you were trying to protect by encrypting it. A powerful solution to this scenario is homomorphic encryption. Homomorphic encryption may finally be the answer for organizations that need to process information while maintaining privacy and security.

What is homomorphic encoding?

Homomorphic encryption makes it possible to analyze or manipulate encrypted data without revealing the data to others. Something as simple as searching for a coffee shop when you’re out of town gives companies access to more metadata than expected. Based on this information, they show you how far the nearest coffee shop is and what time it is when you are searching (when you tend to drink coffee). This is only a tiny part of the valuable information you provide to companies. Also, they cannot understand your answer to the location of the coffee shop and how you got there. If homomorphic encryption is applied to such searches, none of this information will be visible to third parties or service providers such as Google.

However, when a person’s privacy is a priority, homomorphic encryption finds excellent potential in personal and sensitive data areas, such as financial services or healthcare. In these cases, homomorphic encryption can protect the sensitive and confidential details of the actual data but still enable the process of analyzing and processing the information.

Another advantage of homomorphic encryption is that, unlike other encryption models in use today, it is immune to decryption by quantum computers.

Like other forms of encryption, homomorphic encryption uses a public key to encrypt data. But unlike other forms of encryption, it uses an algebraic system to allow operations on data while it is still encrypted. Then, only the person with the matching private key can access the unencrypted data after the operations and manipulations are completed. This solution makes the data safe and confidential as long as someone uses them and remains secure and confidential.

There are three main types of homomorphic encryption, semi-homomorphic encryption (which keeps confidential data secure by only allowing some selected mathematical operations to be performed on the encrypted data), semi-homomorphic encryption (which supports limited operations that can only be performed a few times ) and fully homomorphic encryption (this is the gold standard of homomorphic encryption that keeps information secure and accessible).

Dr. Craig Gentry describes homomorphic encryption as a glovebox where anyone can reach into the glovebox and manipulate what’s inside but not be able to extract the contents. They can take the raw material and make a change in the box. When done, the person with the key can delete the material (processed data).

Practical applications of homomorphic encryption

While cryptographers have known about the concept of homomorphic encryption since 1978, it took off when Dr. Gentry developed the algebraic homomorphic encryption system for his graduate thesis and designed the first homomorphic encryption scheme in 2009. Homomorphic encryption can make the way searches are entered into search engines more private, but there are other practical uses when data is in service or transit.

In highly regulated industries, the secure outsourcing of data to cloud environments or partners with whom data can be shared for research and analysis is a significant challenge. Homomorphic encryption can change this situation, enabling data analysis without compromising privacy. This type of encryption is effective in various industries such as financial services, information technology, healthcare, etc.

What are the obstacles to using homomorphic encryption?

The biggest obstacle to the large-scale adoption of homomorphic encryption is its slowness; In such a way that it is still not practical to use it for many applications. However, companies like IBM and Microsoft and researchers like Dr. Gentry are trying to accelerate this process by reducing the computational overhead required for homomorphic encryption.

last word

On the one hand, the impressive and increasing success of social networks, online shopping, digital assistants, and technologies that require user personal information show that most users have no problem sharing their data; on the other hand, laws and standards also play a role. They will play an essential role in how large organizations use data to change behavioral patterns.

Gartner believes that if Internet-behavioural projects do not provide added value to the user, the entire concept of you will fail. Privacy concerns also need to be adequately addressed so that you can be widely adopted in the future.