Partisia Blockchain

Partisia Blockchain is a Web 3.0 public blockchain.

Partisia Blockchain is a Web 3.0 public blockchain.

Partisia Blockchain is a Web 3.0 public blockchain.

Partisia (MPC) is a public Web 3.0 blockchain built for trust, transparency, privacy, and fast turnaround times.

The decentralized Internet, as a seamless channel of information and the basis for innovative business models, has created a digital economy that is centered around a small number of successful platforms built and secured by built-in network effects. The huge success of the internet economy has created some of the most valuable companies in the world. However, the flip side of this is that these companies are accumulating vast amounts of personal data of individuals, which in turn has a strong impact on the global distribution of power and wealth. WEB 3.0 should solve this problem by providing a more neutral platform or infrastructure that empowers the human, especially as the Internet economy is rapidly expanding into all areas of value transfer, including sectors, in which data security is an absolute prerequisite, such as banking, insurance. and telemedicine.

The blockchain itself is currently being deployed to provide a more secure and neutral platform for the internet economy. While existing blockchains make a positive contribution to providing a truly secure infrastructure, one of the most important trade-offs they present is the lack of privacy. Without privacy, the potential of blockchain to disrupt intermediaries or third parties will be limited by the inability to achieve compliance with established industry standards and best practices, resulting in reduced adoption and ultimately inability to affect the balance of power or data control. This is recognized by many central players in the blockchain industry. One clear indication of this is the widespread use of zero-knowledge proofs as an important first step towards privacy. Zero-Knowledge Proofs: However, limited to one party (the verifier) ​​entering a secret input to compute true or false, which is very useful for simple operations such as confirming a private transaction. However, any collaborative solutions that involve more parties require shared encrypted computing technologies that do not rely on trust in any individual or organization. which is very useful for simple operations like confirming a private transaction. However, any collaborative solutions that involve more parties require shared encrypted computing technologies that do not rely on trust in any individual or organization. which is very useful for simple operations like confirming a private transaction. However, any collaborative solutions that involve more parties require shared encrypted computing technologies that do not rely on trust in any individual or organization.

Parallel to the release of the new Bitcoin blockchain in 2008, the Partisia Blockchain team led the first large-scale and commercial use of another type of distributed cryptography, Secure Multilateral Computing (MPC). Unlike blockchain, MPC provides privacy through a network of computing nodes that compute directly on encrypted data with zero knowledge of the data. The opportunity presented by the potential merger of these two technologies has long been recognized and the Partisia Blockchain team has been working on a solution to this problem for the past 3-4 years. Today we are pleased to introduce this solution to the world - the Partisia blockchain.

The underlying architecture of Partisia Blockchain provides an efficient foundation for zero-knowledge computing and a unique balance between transparency and privacy when using zero-knowledge computing such as MPC. The Partisia blockchain provides an unprecedented balance through an architecture with two separate layers and networks:

-Public and transparent blockchain layer programmed through public smart contracts;

-A private layer of zero-knowledge computing, programmable through private smart contracts.

The first commercial version of the Partisia blockchain was released in September 2019 and functions as a commercial grade infrastructure for both data and market solutions. The newly established Partisia Blockchain Foundation, based in Zug, Switzerland, aims to launch Partisia Blockchain as a global WEB 3.0 platform and as a second layer in existing blockchains. Another key part of the Partisia blockchain is Oracle, which uses threshold cryptography to orchestrate the transfer of values ​​between blockchains. It is provided by Partisia Blockchain Foundation member Sepior, a subsidiary of Partisia that has been creating commercial threshold crypto since 2013 and in recent years in close collaboration with Japanese financial services giant SBI Holdings.

In 2003, I was working on a PhD in auction design and studying the so-called “revelation principle” when Jakob Pagter (now CTO of Sepior) introduced me to Ivan Damgaard (one of the original MPC creators and co-authors). creator of the Merkle-Damgaard design), who was able to calculate encrypted information without a single point of trust. The "revelation principle" is a Nobel Prize-winning epiphany that states that: "It is at least as good as any economic mechanism to rely on direct exposure, where the parties involved tell the truth to an impartial intermediary who uses the information received in the best interest. sides." Since "impartial intermediary" is a common concept in economics, it came as a big surprise to me that this concept also appeared in computer science, where it is known as MPC. It was, and still is, a perfect match, and it even turned out that computer scientists and economists have been working on these two sides of the same coin entirely separately since 1979.

By combining these two impressive sets of knowledge, we convinced the Strategic Research Council in Denmark to invest in this potential to improve resource allocation and collaboration in general. As part of this work, we engaged industrial partners, including Danisco, one of the largest Danish companies at the time. We knew they had a problem that we could solve, but we had no idea how important a role we would end up playing. Danisco was a conglomerate that was planning to sell its sugar division (half of the company's revenue), which was severely hampered by a severe drop in prices. These changes required an immediate redistribution of production contracts between farmers supplying raw materials in the form of sugar beets. Although most thought that the market would decide the redistribution, this did not happen. It was clear to everyone that market inefficiencies were growing, but no one could agree on a price and nothing happened. The farmers did not trust Danisco (the monopsonist) and they did not want to share any information that could reveal the true value of the contracts. This was the main problem we were preparing to solve with MPC by replacing the auctioneer with an MPC-based decentralized exchange. The situation in the blocked market was a growing concern as the Danisco split approached and they finally gave us a chance to solve the redistribution problem. 1,200 farmers participated in the initial auction, and the estimated market price was as close to zero as possible. However, it is not surprising that the market was blocked, now the price came from an unbiased scheduler - a decentralized exchange - and no one questioned the legitimacy of the result. Several auctions later, the production contracts were reallocated and the sugar division was sold to North Sugar in Germany.

The decentralized exchange case was a fantastic opportunity that allowed us to showcase the power of MPC in a full commercial environment. It worked and solved a big problem for the client who wanted to keep using the service - and Partisia was created.

This was just after the 2008 financial crisis - but similar to the ideas behind blockchain - we also saw a lot of weaknesses in the financial system that we could address with MPC. However, it was still very early in the life cycle of this new technology, but despite less efficient protocols, applications such as auctions provided a natural time window for heavy computation. Working with auction solutions for production contracts, power licenses and radio frequency spectrum, we naturally ran into the next big challenge - key governance issues. Key management has quickly become an area where MPC can provide scalable solutions and we decided to explore this further - and Sepior was created.

In parallel, and over the past 10 years, collective efforts to develop better protocols and frameworks have reduced the computational overhead from MPC by 1/1,000,000, gradually opening up more scalable applications such as advanced data processing solutions and real-time matching services.

Now Partisia and Sepior are once again joining forces, bringing expertise and software from the marketplace, data and key management applications, and many infrastructure components to the Partisia Blockchain.

Blockchain Partisia: neutral WEB 3.0 platform

When Ethereum was launched, we were replacing intermediaries with MPCs and creating more trusted market and data solutions, which really inspired us to think about merging blockchain and MPC. On an intuitive level, it looked perfect right from the start. Blockchain is all about transparency and storing data in an immutable database. Transparency can create trust by shedding light on the MPC as a black box without compromising privacy, such as who is bidding, who controls the MPC nodes, and what is being calculated. The immutable database has in many cases been the ideal place to output MPC calculations such as price and auction winner. However, it wasn't until we started building blockchain solutions that we realized the full potential of the blockchain as the basis for MPC orchestration.

When the blockchain ecosystem began to flourish, we were approached by Insights Network and started building a data exchange solution based on blockchain and MPC. Later, we expanded our collaboration with another client, Tora, and moved the MPC-based matching service we had been working on with them to the blockchain - the Cyberian project. These two projects pushed us to think seriously about how to push the boundaries to merge blockchain and MPC on a more fundamental level - and the Partisia Blockchain project began.

From an economist's point of view, the infrastructure and application of blockchain and MPC is a goldmine of opportunity. The following three main components make it an ideal toolkit for materializing solutions for better resource allocation and more valuable data collaboration:

-With a decentralized immutable ledger, blockchain eliminates the traditional middleman.

-With zero-knowledge decentralized computing, MPC removes the traditional trustee as an intermediary.

-With the help of smart contracts, developers and users can control and automate the use of the infrastructure.

While the Partisia blockchain provides all of these components, it is designed to operate as a fully managed second layer that adds privacy and greater interoperability to existing blockchains as well as autonomous systems. To further enhance the collaborative approach, the Partisia blockchain is designed to use any existing coin as a means of payment. This value transfer component is also present on the testnet, which transfers EOS tokens back and forth to Ethereum.

Infrastructure has significant potential to solve problems in new ways, using the option of decentralized and centralized decision making. If we remember the “revelation principle” for a moment, then the main trick is to centralize decentralized solutions that provide the most optimal distribution in general in a decentralized market economy. The Partisia blockchain provides the tools to do this and the potential to improve markets and overall negotiation solutions for most industries and applications.

When we talk about data processing solutions, one of the key issues is control over data and algorithms, not least with artificial intelligence. Thanks to the decentralized approach to privacy enabled by Partisia Blockchain, application developers can create solutions that preserve the privacy of participants' personal data while ensuring full transparency of the algorithms used. In the short term, this better data protection allows the most valuable but sensitive data to be more widely used (not shared) in a secure and interoperable manner. Ultimately, this could change the power structures that operate in today's Internet economy and allow greater competition at the application and service level.

Infrastructure-level competition is built into the Partisia blockchain as a decentralized autonomous network run by independent computing nodes. In addition, interoperability promotes competition between alternative networks.

You may have heard of Multilateral Computing, or MPC for short, but you may not know exactly what it is. That's why in this blog post, we're going to explain what multilateral computing is and discuss the deep and seemingly impossible tasks it can help us solve.

Multilateral computing has a number of real world applications. Let's say for example we have a group of people who have the same job in different companies. They are interested in knowing what their average salary is in order to negotiate salary in the future. On the other hand, for obvious reasons of confidentiality, they do not want to simply reveal their salaries to each other.

So the question is: can these people interact in such a way that everyone can find out what their average salary is, but in such a way that no one finds out more than that? At first glance, this may seem impossible. How can we calculate data if it is not available to us? However, it can be done, and here's how.

First, we see that we really only need to calculate the sum of all salaries. If we have the sum, we can divide it by the number of participants to get the average, and vice versa, if you know the average, you can multiply it to get the sum so that the sum does not show more than the average.

If, for example, we have 5 people involved, we call them A, B, C, D, and E. We'll let X stand for any of these people. Now, in order for participant X to enter his salary S into the calculation, he does the following: he chooses 5 random numbers x1, .., x5, but with the property that

S = x1 + x2 + x3 + x4 + x5

This can be done, for example, by randomly choosing the first 4 numbers and then choosing the last one so that the sum is correct. Now you privately send x1 to A, x2 to B, and so on (you also give yourself a number).

Note that this doesn't reveal your salary to anyone: for example, C gets x3, but it's just a random number. To understand this better, consider a really simple case involving two people, Bob and Charlie. Let's say the secret number is 3 and we give 5 to Bob and -2 to Charlie. Now, from Bob's point of view, he has no idea what number Charlie got. And since 5 plus anything that can produce any result, Bob still doesn't know what the secret number is.

This is an example of so-called secret sharing, which is one of the most basic and important tools of MPC. In this example, the secret is S and the xi are called shares. For readers with a mathematical background, I should mention that for the description above to be correct, one must actually choose a random xi modulo some sufficiently large number p, and then one must perform addition modulo p. But I'll ignore it here to keep it simple.

So, in MPC jargon, in order to feed your salary into the calculation, member X will secretly share his salary to get shares of x1,…,x5 and give one share to each member.

Now we are finally ready to see how secure computing is done. After each member secretly shared their salary, each received one number (share) from everyone else. In tabular form, the shares held by A, B, C, D, and E are as follows:

So, for example, (a1,…, a5) are shares of A's salary, so they are added to A's salary. Now each participant adds up all their numbers (shares) and sends the received amount to all other participants. In other words, the sum of each column in the table is calculated and published, for example, the results as sums of columns s1,…, s5.

Now it turns out that the sum of all salaries is simply s1 + s2 + s3 + s4 + s5.

To understand why, note that if we added each row in the table, we would get salaries, and adding them gives the result we want. So this result is actually the sum of all the numbers in the table above. But now notice that when we add the columns and then add the sums of the columns, we just add everything in a different order. But, of course, the amount is still the same!

The protocol we've seen is a good example of a general principle often followed by MPC designs: Secret data is entered first. And now, instead of calculating the actual data, the participants perform the corresponding calculations on the shares they have. And from the (partial) results they get, we can finally recover the actual result we want. This keeps the input data private because we only compute the shares that don't reveal anything about the actual data.

In real life MPC uses more advanced methods where:

-we can do general calculations, not just sums.

-we can complete the work even if some members fail or break.

-we can at least identify those who didn't do what they should have done.

However, the overall design pattern is still very similar.

The problem with the average salary is just one example. It turns out that in real life there are many situations in which MPC can solve the problem. Let's look at some of these situations to see what they have in common.

Auctions come in many varieties and are used for a wide variety of purposes, but for this, let's focus on the simple case where an item is up for sale and where the highest bid wins. We assume that the auction is online (similar to the ones that happen on Ebay) where the price starts at some pre-set amount and people bid increasing until no one wants to bid more than the current highest bid. Since this is a time-consuming process, most of these online auctions allow you to enter the maximum amount you are willing to pay and the system will bid on your behalf up to that maximum bid. Therefore, the bidder with the highest selected strike price of the item wins the auction at a price slightly higher than the runner-up price,

Obviously, for such an auction to work honestly, the maximum amount you are willing to pay must be kept confidential. For example, if the auctioneer knows your maximum exercise price and works with the seller, he can force the price to be just below the highest maximum bid and thus force the winner to pay more than if the auction were fair.

On the other hand, the outcome of an auction can, in principle, be calculated by considering the true maximum value that each bidder assigns to the item being sold.

A typical purchasing system is a kind of flip auction in which some party (buyer) asks companies (sellers and bidders) to bid for a contract, that is, to make a bid for the performance of certain work. In this case, the lowest bid wins. But on the other hand, bidders are interested in getting the highest possible price.

Let's look at the simplest possible auction with one round of sealed bids, in which the lowest bidder wins and is compensated according to the lowest bid (first price sealed bid auction). Clearly, the bids are private information, since the seller with the lowest bid may benefit from a bid increase to just below the second highest bid. In addition, if competing sellers have access to the identity of bidders and their sealed bids, it will be easier for them to undermine competition and negotiate prices outside of the auction.

On the other hand, the true value of the bids is needed to find the winner of the auction.

Let's say you run a company. You will naturally be interested in how well you perform compared to other companies in the same field as yours. Comparison can be related to a number of different parameters such as profits in relation to size, average wages, productivity, etc. Other companies are likely to have similar interests in such a comparison, which is known as benchmarking. Such an analysis requires the participation of all participating companies. Based on this, he tries to compute information about how well a company in a given line of business should be able to perform, and finally, each company is told how its performance compares to this "ideal".

Understandably, each company will insist that its own data is kept confidential and should not be shared with their competitors. On the other hand, desired results can be calculated from private data, and there are several well-known economic methods for performing such an analysis efficiently.

In most countries, government agencies, such as the tax authorities or the health care system, maintain databases containing information about citizens. In many cases, there are benefits to be gained from coordinated access to multiple such databases. Researchers may be able to obtain statistics they would otherwise not be able to obtain, or institutions may benefit from an administrative advantage by being able to quickly collect the information they need about a particular individual.

On the other hand, there is clearly a confidentiality issue here: access to many different databases by one party opens up the possibility of compiling complete dossiers on specific citizens, which would be a breach of confidentiality. In fact, access to data about the same person in several different databases is prohibited by law in several countries precisely because of this problem.

Answer? MPC.

In all of the above scenarios, we see very similar situations: we have multiple parties, each with some personal data. We want to perform some calculations that require all personal data as input. The parties are interested in receiving the result, or at least part of it, but still want to keep their personal data confidential.

A very simple (but naive) solution to this problem would be to find some party, T, that everyone will trust. Now all parties privately transfer their data to T, she performs the necessary calculations, announces the result to the parties, and forgets about the private data she saw.

However, in reality, this is very unsatisfactory: firstly, we created a single point of attack, from where all personal data could potentially be stolen. For example, the party that manages sealed bid auction bids is such a critical point. Second, all parties must have full confidence in T, both for confidentiality and for the correctness of the results. The reason for the privacy concern is that the parties do not trust each other in the first place, so why should we believe that they can find a new party that everyone trusts?

On the other hand, the problems we've seen are exactly the kind of problems that MPC can solve, but without creating a single point of attack. It is clear that the list of potential applications of MPC technology is essentially endless.

Partisia is built for trust, transparency, privacy, and speed of light finalization by combining secure multiparty computation (MPC) and blockchain.