GeoDB

ODIN is a decentralized system designed to build a data oracle network (DON) based on an open protocol for interaction between participants and a sustainable economy. In addition to organizing the data oracle network, ODIN involves building a decentralized peer-to-peer data sharing and trading ecosystem. The use cases of

ODIN are mainly focused on user generated data apps, Non-fungible tokens, Decentralized Finance applications and others. ODIN is a new system that combines the advantages of cryptography and decentralized technologies, as well as the simplicity of connecting data providers and the flexibility of developing contracts to receive and process data from them (to work with business requirements, which are to increase the scalability and reliability of circulating data).

By default, each accounting system is deterministic within its own boundaries. Simply put, the system can be guaranteed to trust only those events and information that are the product of the life of the system. However, when it comes to obtaining external data (confirmation of payments, external statistical information, etc.), we come to the need to use an additional entity - an oracle, which will be the provider of such information (a bridge between the accounting system and the outside world).

GeoDB (GEO) is a global big data marketplace that rewards you for the data you generate.

You may not know this, but several companies are now using your personal data. And there's something they're especially interested in - your location. In order to know their environment, study their competitors, identify opportunities and trends, optimize their supply chain and more, companies analyze large amounts of data every day and in many cases location is absolutely essential to contextualize the data and make useful analysis.

The above may not surprise you, or maybe it does, but what you probably don't know is that several studies have proven that location is the personal data that worries us the most. And how much do companies pay you to use your most sensitive personal information? We don't want to disappoint you, we know there are several types of rewards, but we just want to ask you this question so you can think about it and its implications.

Competition is the main concern of any business. Online platforms are places where your privacy is at risk. Whether it's online banking, grocery stores, clothing stores, or food apps; everyone is mining your data. In most cases, this happens without your permission. However, even if they have your permission (hidden in their long pages of terms and conditions that, frankly, none of us pay attention to), you get nothing in return! Have you ever wondered who is doing this and how much money they make from your data?

Data brokers sell user data to other companies and make up a $200 billion market.

And at what cost? Our data and privacy. Your next question will be, what is the benefit for any company from my data?

They say that data is the new oil, which is very true in the internet age. Companies mine and sell data for big data analytics. The analysis shows that the size of the big data industry will reach US$100 billion by 2027.

Have you ever thought about how companies use your personal data?

Usually they are not up to something gloomy, rather the opposite. It's hard to make a precise classification, but we could say that our data is useful for things like

-offering better services;

-supply chain optimization;

-increase in clientele;

-opening up new possibilities.

-Things like this allow them to:

Offer the best mobile phone coverage;

-Minimize costs to give low prices;

-Build supermarkets and gas stations in busy areas with easy access;

-Optimize navigation routes and estimate their duration;

-Provide better roadside assistance;

-Offer fast and cost-effective package delivery services;

-And much more.

But let's not just think about the private sector, let's also think about everything that's possible in the public sector. The use of these tools in public administration allows them to:

-Build hospitals and schools in the best places;

-Lay out suitable evacuation routes;

-Find the best places for emergency services;

-Deploy the necessary infrastructure.

-For your peace of mind, you should also know that no one is interested in knowing your individualized data, in fact an isolated person's data is useless for this type of analysis. This is a collection of data from several people, which allows you to respond to situations like the previous ones.

The core of the problem is not how our personal data is used, but how it is collected. It is unacceptable for companies to collect and use our personal information without our consent. And it is unethical that, using psychological tricks, they can get great economic benefits by paying for raw materials with peanuts.

The collection and use of personal data based on fraud cannot remain the norm. This approach generates suspicion and rejection on the part of users and, more importantly, opens privacy gaps.

A clear message must be conveyed to users so that they understand that it is possible to provide their personal data through the correct channels:

1.Give them the right to decide how to use them.

2.Avoid privacy risks.

3.Get a fair economic reward.

4.Enjoy optimized services.

We must demand control over what we sell, on what terms and at what price. Money we demand in exchange for our personal location data.

How much money will you sell your personal location data for every five minutes for the next month? It's a delicate question, isn't it?

In 2013, the Financial Times published an interactive calculator that allows us to price our personal data based on indicative prices provided by data brokers.

Playing with the calculator, we get values ​​from half a dollar to two dollars. For simplicity, let's assume that the average cost according to this calculator is about $1.

Buyers are not interested in individual user data, but in large datasets with data from millions of users. Based on the ratio 1 person = $1 , it is easy to estimate the value of a million user data.

However, the previous value is calculated based on the price quoted by the Financial Times, but would you be able to sell your personal data for $1?

The problem is that companies ask questions and respond at the same time, asking a question and giving you an answer. Setting such a price is a way of self-justifying the use of indirect formulas to collect user data. "How can I convince a user to give me their personal details in exchange for a dollar?".

It is in our own interest to answer this question for ourselves, and researchers have already tried to answer it honestly.

Ancient and Frog Design conducted a study in 2010 to quantify the value of personal data people are willing to give up in exchange for IT services, and the results are summarized below.

The study shows two interesting findings:

1. We want much more than a dollar in exchange for our personal data.

2. We value certain types of information more than others.

By removing data from this study that can only be disclosed once, i.e. social security number, government ID, credit card information, social profile, contact information, and demographic information, we find that the most valuable private data generated daily is :

-History of Digital Communications - $59;

-Web History - $57;

-History of physical location - $55;

-Web Browsing History - $52.

This difference in the assessment of the value of information has been confirmed in subsequent studies. In particular, Staiano, J. et al. in their 2014 paper "Money Walks: A Human-Centric Study on the Economics of Personal Mobile Data" found evidence that location is the most valuable category of personally identifiable information, and this voluminous information is valued much higher than individual information.

But is that the price of our personal data? No, this is the price for which some users were willing to reveal their personal data under certain conditions.

The value for which we are willing to disclose some information about our private location was studied in 2005 by Danezis, G. et al. and in 2006 Cvrcek, D. et al. a study was conducted in which, using deception, the authors asked several people if they were willing to provide data about their private location in exchange for money. The study allowed them to measure various factors, such as how many users were interested, how much money they demanded, or how expected data usage impacts price.

This study showed some interesting results, but it was done on a relatively small scale at the University of Cambridge, so a new study was done in 2006 using "a sample of over 1200 people from five EU countries and experimental psychology and economics tools to draw from them the value they attach to their location data. The sample size allowed them to "compare [value] across national groups, gender and technical awareness, and the perceived difference between academic and commercial use.

We highlight below some interesting findings from this study:

-Women may be more sensitive to what the collected data can be used for.

-Participants did not consider their unusual movements to be more sensitive than their daily behavior.

-Participants were more sensitive to the purpose of data collection than to the duration and amount of data collected.

-There are huge differences between countries in sensitivity to time extensions.

-The main results confirm those of the Cambridge study on total application costs - for example, median applications are £20 and €43 (ie about £28 at the August 2006 exchange rate) for non-commercial use of the data, respectively.

Well, it looks like we expect to get a lot more than $1 for our personal data. Don't you think so? And we have to keep in mind that these results are from 2006, as prices rose that year, user awareness of mobile technology increased, and location became the most valuable category of private information. We are sure that currently the price will be higher, but we do not want to bid, you decide yours.

We seem to have slightly increased the amount of money a user should receive as compensation for sharing their personal location, but is a user's personal location really that valuable?

The big data market was worth $125,000,000,000 in 2015, giving an idea of ​​how much financial capital companies are investing in data operations. But big data is not only about collecting and processing huge amounts of data. If the data you store and analyze is full of inconsistencies, inaccuracies, or other problems, your analytical results will be misleading.

It is estimated that 80% of business data contains a location component, so it is important to understand how this affects the business. Their analysis can provide insights that support and improve decision-making procedures in many aspects of a business. Analyzing data by location allows businesses to ask questions and accurately answer questions like "where are my customers?" or "how far are my clients from my location?" and "How well is my supply chain serving these customers?".

Thanks to the ubiquity of smartphones, it is possible to bring order to the analyzed data by adding location information to them; if you can contextualize your data in this way, you can “Reveal relationships between datasets that would not otherwise be obvious or easy to establish, and use location analysis to arrive at the information that is reflected in the total row.”

The last part of the previous paragraph comes from a Forbes Insight article published in 2017 in which they conducted several interviews with various executives who use localization in their big data analysis. In the article, managers openly say that this information is in many cases a critical element for conducting analyzes that do not give misleading results.

We would like to highlight some parts of the interview with Nigel Lester, Managing Director of Pitney Bowes in Australia-New Zealand, as we believe they masterfully identified the importance of localization for big data analytics.

“The real power of location data is that it becomes the link between seemingly disparate business data stores. Data that does not have an obvious relationship can be contextualized by location. It could be your customer locations versus your competitor's locations - datasets with no obvious connection, but if you start geo-enriching them, you may find relationships start to form and you can build a more cohesive and valuable base of your customers."

GeoDB is a decentralized peer-to-peer big data exchange ecosystem that returns value to its creators, users.

We will look at an example here to better understand.

You have found a pop-up window "Company XYZ wants to know your location."

Can you guess how much data you generated by clicking this button?

Usually, to specify a location, we need parameters such as latitude, longitude, timestamp, and other information related to the mobile network. Let's say you split it into half a day; Our research shows that you generate approximately 17 KB of data. Now imagine this for millions of users within 5 years. The numbers will be as follows.

This is the amount of generated data! A total of 12,472,540.14 GB of data for 100 million users over 21 years, which will generate an annual revenue of $68,403,553,920.

All this happens without the knowledge and consent of the data creator to sell their data. GeoDB not only uses the data of an authorized user, but also compensates for them. With full control of users over their data, while maintaining their anonymity.

GeoDB maintains data confidentiality and integrity, and how does it compensate its users? This is achieved through data decentralization. We have a digital data system called Digital Ledger Technology (DLT) that stores digital data geographically dispersed across countries.

However, using a traditional DLT (distributed ledger) has several disadvantages such as:

-Storing data in a public DLT is expensive. For example, if you are considering storing one GB of data on bitcoin blockchains it would be $60,000,000.00.

-It has data transfer capability but no storage capability.

GeoDB uses a hybrid DLT system and process flow to overcome them.

-It uses federated DLT, public DLT, and most importantly, the DLT blockchain to keep the user's identity anonymous.

-GeoDB initially has full federation and then gradually adds trusted members to it.

-Data from participants enters the system through SDKs installed on Geo smartphones.

-The data is then checked for quality assurance, reliability and ownership. This is done by the authorized DLT and after that the reward is calculated by the federation.

Geo-Cash: A handy app for earning and tracking geo-tokens that can be easily downloaded from the app stores for Android and iOS devices.

The application, with the consent of the user, anonymously processes data about the location and other devices of the user. Rewards are governed by rules set by the federation. So, how can you pay an anonymous person? This is where blockchain and cryptocurrency come in.