Throughout the ages, famous philosophers have grappled with the concept of good governance. From Aristotle, Machiavelli, and Hobbes to Rousseau, Voltaire and Rawls, different perspectives have existed and challenged each other over the ages on the topic. Today, in democratic societies at least, the general consensus is that of a government that is accountable to the people, with checks and balances, the guarantees of fundamental rights, and integrity in how it operates. New technologies, such as blockchain, can aid in the pursuit of good governance — this article outlines a few possible examples of how Partisia Blockchain could help governments innovate and better their governance practices:
Paperwork, licenses and standing in lines — bureaucracy is something that regardless of political affiliation, people love to hate. But the true purpose of bureaucracy (whether well-designed or not) is to ensure due process and guarantee people’s rights. This in essence very noble pursuit can run into a variety of different problems, from potential inefficiency to outright corruption. A public blockchain could help to streamline processes and make them more transparent, paperwork can be filed and traced through different steps on the blockchain, whereas combined with MPC the private information in these processes can be kept secret, or only available to certain parties. In certain countries, where corruption is an issue, the intransparency of bureaucracies can allow for wrongdoing in e.g. bureaucratic processes such as ignoring, changing and/or the outright fabrication of documents. A public blockchain could allow for more trust in bureaucratic institutions, especially if those institutions don’t have control over the nodes that operate the blockchain. This is the principle behind a project called DelNorte.
DelNorte is currently running pilot projects in Latin America creating NFTs out of real estate deeds and adding them to a public blockchain. This is meant to make the bureaucratic process more efficient, give more stability and transparency regarding real estate ownership in the participating countries, circumvent potential corruption and maintain the integrity of the institution. While the government is the door to access to the system, the government does not have control over the blockchain and the listed real estate deed NFTs. Partisia Blockchain is proud to have entered into a partnership with DelNorte, helping them to add privacy and security to their e-government solutions.
Governments provide goods and services to their citizens, from parks, highways and schools to militaries for the national defense. While some governments have more resources than others, many of the goods used to e.g., build and maintain a public highway, need to be contracted to third parties. What is usually the case when a government has to contract such goods or services out, is that they publish a tender for which parties can bid. This ideally leads to many different companies bidding for the contract with the government, attempting to underbid each other and/or outclass each other with the quality of the good/service that they provide.
Nonetheless, public procurement bidding processes are often highly intransparent and even prone to corruption, which cheat the taxpayers out of the best possible deal they could have had. Blockchain technology could also help combat this problem, making the bidding process transparent and establishing trust with the general public. However, a major issue with the transparency of a public blockchain is that it does not allow for the hiding of certain sensitive information e.g., a company’s capabilities, classified technology, etc. that could be part of the bidding process. This is where E-Trusty comes in: E-Trusty is a dApp building on Partisia Blockchain to use the public blockchain to create transparency, while obfuscating sensitive information in the bidding process using MPC. The goal is to create a platform for public procurement that allows for the transparency of seeing multiple bids for a given contract, while using MPC to hide and protect sensitive information.
Multiple central banks around the world are beginning to develop and implement so-called central bank digital currencies (CBDCs). As opposed to digital currencies, such as Bitcoin or Ethereum, these digital currencies are centralized and issued by a national bank. They are pegged to the value of a fiat currency and are meant to be a part of the existing financial system. There is however a major concern regarding CBDCs and that is that due to their centralized structure and control, they could essentially allow for a central bank, and by extension a government, to have complete insight into how people are spending their digital money. Furthermore, it is also feasible to imagine that a government could easily overreach, especially if it were to become corrupt, and easily seize such digital money. There would therefore need to be checks and balances guaranteed in the application of a CBDC. One solution for this problem, could be to use MPC to make the settlements of such a CBDC private. Such a system could also be designed to allow for certain transparency towards a government entity with the sufficient legal justification such as a warrant. The CBDCs settlements would be intransparent to e.g. the national bank or the government, however a court could allow for access to certain transaction data for a judicial institution.
In many places across the world, trust in elections is waning: the intransparency of voting systems, combined with distrust fueled by political rhetoric are a major threat to the integrity of democracies today. The recent coup in Bolivia or the storming of the U.S. Capitol have shown that even an unsubstantiated claim of fraud in an election can lead to political violence or even the overturning of a democratically elected government. E-voting, and particularly blockchain-based e-voting solutions, have attempted to solve this issue. They have however run into a variety of problems: intransparency or too much transparency, hardware and/or software vulnerabilities, among many others. Nonetheless, Partisia Blockchain’s MPC technology could help in solving many of these issues. MPC could be used to assure the privacy of a voter’s ballot, while showing votes being tallied for specific candidates in real-time. The election results could be publicly auditable and contestable and voters could be able to track their own votes. This kind of solution could in theory ensure safe, transparent and auditable elections, while keeping people’s votes secret.
Partisia Blockchain Foundation is dedicated to facilitating innovative solutions to real-life problems. Democratic innovation is one of the fields we are proud to contribute to.
Please contact us, if you have any questions about how our technology could enable better governance or if you think your organization could benefit from our technology.
Contact information: build@partisiablockchain.com
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In 2018, the European Union’s General Data Protection Regulation (GDPR) came into effect, causing a wave of changes to terms and conditions in your favorite applications across the globe. GDPR aims to increase people’s control and rights over their own personal information and heavily penalizes companies that infringe on these rights. Infringing on the rights of EU citizens laid out in GDPR could result in a fine of €20 million or 4% of the annual global turnover of an enterprise, so compliance is strongly incentivized. This new regulation is widely considered a major turning point in data protection and privacy rights, starting a policy diffusion of similar data protection laws across the globe. GDPR is law in every member country of the European Union and establishes a “single data market” within the EEA. Similar regulations have also been adopted in California, Chile, Japan, South Africa, Argentina, Turkey and Brazil, among others.
GDPR (as well as many of the similar regulations) involves multiple core tenets, among others setting out the principles for which personal data can be used and processed. Lawful purposes of the use of personal data and the digital rights that citizens have over their personal data. While there are many different compliance aspects of data protection regulations, such as GDPR, here are a few examples of how our technology could help your organization stay compliant:
GDPR requires organizations processing personal data to transform the data in such a way that it cannot be connected to the person it was collected from (pseudonymization). Partisia Blockchain could help an enterprise disassociate a person from their (encrypted) data, assuring such pseudonymization through the use of multiparty computation (MPC) technology. This pseudonymization can also be done in a way to allow for continuous collection of data from the same individual, if required for e.g. a longer-term study.
Furthermore, the concept of MPC also can also aid in maintaining an individual’s control over their data, as e.g. the concept of MPC secret sharing can allow for useful outputs being generated without compromising the underlying data (see Multiparty computation: The beacon of privacy solutions explained). MPC (especially combined with a blockchain) can also therefore increase the security of personal data, as the data and calculations are all run in a decentralized fashion by nodes that are all independent from each other. Partisia Blockchain’s nodes and their operators are all independent, run independent systems and have been vetted for cybersecurity by Partisia Blockchain experts.
Another right laid out by GDPR is the so-called right of access. This is the right of people to be able to see how their data is being processed and with whom it is being shared. The ledger kept on a blockchain could help an organization provide an immutable record to ensure this right. For the same reason, the blockchain could help organizations provide the record of processing activities required for GDPR-compliance under certain circumstances as well. As opposed to some other blockchains, Partisia Blockchain also allows for the possibility of private data to be removed from the record. Essentially meaning that data entered into the blockchain can be erased later on, allowing for compliance with GDPR’s right of erasure (the right for people to have their personal data removed from a database).
Lastly, the geographical location of servers used to process personal data could sometimes mean the difference between compliance and a criminal offense. Partisia Blockchain’s jurisdiction management v1.0 allows organizations’ developers to specify the geographic location of nodes to be used in calculating personal data. This could for example allow for private data from the EU to only be sent to EU-based nodes, ensuring that the integrity of the single data market and the data rights of EU-citizens are not breached.
Partisia Blockchain is committed to empowering others in solving real-world problems using our cutting-edge technology. Data rights and data privacy challenges are two of these problems.
Please contact us, if you have any questions about how our technology could enable data privacy or think we can help your organization in improving its data protection architecture.
Contact information: build@partisiablockchain.com
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The point of this document is to provide the shortest (and most intuitive) possible introduction to each of the technologies mentioned in the title. I hope I succeed in this endeavor.
The technologies in this document all — with exception of differential privacy — deal with “secure” computation on data. At a very high level, this means they can be used to perform an arbitrary computation on one or more pieces of data, while keeping this data private.
Secure multiparty computation, which is what we do here at Partisia, is the term for a fairly broad class of protocols that enable two separate entities (called parties) to compute a function, while revealing nothing except the output.
An MPC protocol typically proceeds in three phases: First the inputters secret-share their private inputs. This step can be thought of as each user sending a special type of encryption of their inputs to the nodes doing the computation. The encryption ensures, for example, that at least two out of three nodes are required to recover the input, and thus, we get a security model that relies on non-collusion. It could also be the case that all three nodes must collude to recover the input — in this case, we have a full threshold model (since all servers must collude to break privacy).
The next step involves the nodes (the servers A, B, and C) performing the computation on the encryptions (i.e., secret-shares) received in the input step.
When the nodes finish the computation, they will hold a secret-sharing of the output. Each node’s share is returned to the users, so they can recover the actual output.
As might be inferred from the figures above, MPC works particularly well if the computation nodes are well-connected. Indeed, what makes MPC expensive to run is all the data that the nodes have to send between each other.
MPC have been actively studied in academia since the early 1980s and there are a lot of good resources available to learn more about it:
Fully homomorphic encryption (FHE) solves a very old problem: Can I have my data encrypted and compute on it too? FHE is a tool that allows us to not only store data encrypted on a server, but which allows the server to compute on it as well, without having to decrypt it at any point.
A user encrypts their private data and uploads it to a server. However, unlike a traditional E2EE (End-to-End-Encrypted) scenario, the server can actually perform a computation on the user’s private data — directly on ciphertext. The result can then be decrypted by the user using their private key.
FHE, unlike MPC, relies on clever cryptographic computation, rather than clever cryptographic protocols. On the one hand, this means FHE requires less data to be sent between the server and client compared to MPC. On the other hand, FHE requires a lot of computation to be done by the server.
Practically speaking, FHE is slower than MPC (unless we have an incredibly slow network, or incredibly powerful computers).
Practical FHE is a relatively new technology that only came about in 2009. However, since then it has received quite a bit of interest, especially from “bigger” players like Microsoft or IBM.
Partisia Blockchain supports FHE solutions.
While both MPC and FHE allow us to compute anything, zero-knowledge proof (ZKP) systems allow us to compute proofs. In short, ZKP allows us to compute functions where the output is either “true” or “false”.
ZKPs are incredibly popular in the blockchain space, mainly for their role in “rollups”. The particular type of ZKPs used for rollups are ZK-SNARKs, which are succinct proofs. In a nutshell, a succinct proof is a proof whose size is some fixed (small) constant, and where verification is fast. This makes smart particularly useful for blockchains since the proof and verification are both onchain.
That said, ZK rollups don’t actually use the zero-knowledge property — they only use the soundness and succinctness properties of the proof scheme.
Soundness simply means that it is very difficult to construct a proof that appears valid, but in actuality is not.
ZKPs, like FHE, takes place between a single user and a verifier. The user has a secret and they wish to convince the verifier about some fact concerning this secret, without revealing the secret. ZKPs don’t designate a particular verifier, so anyone can usually check that a proof is correct.
The final private computation technology I will talk about here is trusted execution environments. A trusted execution environment, or TEE, is basically just a piece of hardware that is trusted to do the right thing. If we trust this particular type of hardware, then private computing is clearly doable.
TEEs, being hardware, are tightly connected to some hardware vendor. Often when TEEs are mentioned, what is really meant is something like Intel’s SGX or ARM TrustZone. SGX is the TEE used by Secret Network, for example.
The security model of TEEs is fairly different compared to the other technologies I have written about so far, in that it is a lot more opaque. Vulnerabilities have been demonstrated in different iterations of different TEE products, especially SGX.
Differential privacy is radically different from the previous technologies. (In this discussion I will exclude ZKPs since it does not allow general computations.)
While MPC, TEE and FHE all provide means of computing something on private data, they do not really care about what that something is.
For example, it is possible (albeit pointless) to compute the identity function using both MPC, TEE and FHE.
This is because MPC, TEE and FHE allow us to compute anything. In particular, they allow us to perform computations that are not really private.
At this point, we may ask: Well, why would we perform such a silly computation on private data? For some computations, it might be easy to see that it is not private (in the sense that the original input can easily be inferred from the output). However, there are many computations that are seemingly private, but which can also leak the input if we are not careful. For example, it has been shown that it is possible to extract machine learning models, simply by querying a prediction API. In another example it was shown that it is possible to extract the data that a model was trained on.
These issues all arise because there are no restrictions on the computation that is performed. Differential privacy tries to fix this.
Differential privacy is used to provide a fairly intuitive guarantee. Suppose we are given two databases A and B. The only difference between these two databases, is that a particular entry R exists in A but not in B. Differential privacy now states that, no matter which type of query we make on the database, we will not be able to guess whether we are interacting with A or B.
Naturally, this means that some queries cannot be allowed. For example, it is not possible to obtain differential privacy if one can simply ask “Is record R in the database?”. Generally, differential privacy is obtained by adding noise, or synthetic data, to the database as well as restricting the type of queries that are allowed.
What makes differential privacy different from MPC, TEE and FHE, is that differential privacy makes guarantees about the output of a computation, whereas MPC, TEE and FHE makes guarantees about the process of arriving at that output. In summary:
This also means that differential privacy is not in direct “competition” with MPC, TEE or FHE, but rather complements them.
While each technology has its specific advantages and use cases, it is our feeling that Partisia Blockchain’s MPC, backed by 35 years of research and practical implementation does seem to provide the most overall coverage of all possible scenarios with very little drawback.
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A blockchain, at its very core, is a way for everyone to agree on what the current state of the world is, without having to rely on a trusted authority.
Of course, by “everyone” we don’t actually mean everyone, but instead everyone who believes in the security model. Likewise, by “the world” we also don’t actually mean the world, but rather, whatever is currently written on the blockchain’s ledger. Nevertheless, well-known blockchains such as bitcoin or ethereum both have market caps in the 100s of billions of USD, which tells us that the technology excites people.
Programmable blockchains, in particular, are exciting because their “world” is very rich. On a programmable blockchain, the “world” is basically the current memory of a computer, and so, simply by being clever about how we design the programs that run on this computer, we can use it to accomplish almost anything.
Let’s digress for a bit and classify programs into three categories:
— Those that take a public input and produce a public output
— Those that take a private input and produce a public output
— Those that take a private input and produce a private output
A programmable blockchain such Ethereum supports programs of the first kind: Everyone sees what goes into a smart contract on Ethereum, and everyone sees what comes out again. This is great for some applications (like agreeing on who bought a NFT), but clearly not sufficient for others (like performing an auction).
Several solutions have surfaced which attempt to support the remaining two types of computations. Let’s take a brief look at some of them:
Zero-knowledge proofs (ZKPs) are, in a nutshell, a way for someone to convince (i.e., prove to) someone that they know or possess something, without revealing anything about that something. One situation where this shows up, is when someone wishes to prove to someone else that they control a certain amount of tokens.
ZKPs can therefore be used for private-public and private-private computation, to a limited degree. ZKPs can only compute, well, proofs. This in particular means that the computations are limited to a binary “yes” or “no” output. Moreover, ZKPs are inherently single-user oriented, so it is not possible to perform a computation that takes multiple private inputs.
Note that a program that takes a public input, but produces a private input does not make sense. If everyone can see the program and what goes into it, then everyone can obviously see the output as well.
Another private computation technique is fully homomorphic encryption, or FHE as it is called for short. At its very basic, FHE is a way of encrypting data such that it is possible to perform computations directly on the encryption.
This immediately tells us that FHE for sure supports private input private output type computations.
However, FHE, like ZKPs, are oriented towards a single user scenario. This means that, although FHE can perform any computation (which ZKPs cannot do), they cannot perform a computation that receives private inputs from multiple users.
In contrast to the two above technologies (as well as the next one), trusted execution environments (shortened as TEEs) are a purely hardware based solution to the private computing problem we’re looking at.
A TEE is simply a piece of hardware that have been hardened in certain ways that make it hard to break into. If we believe this to be the case, then a TEE can be used to perform the private input, public/private output computations we’re interested in.
Inputs are encrypted using a key stored only on the TEE, and computations take place on the TEE after decryption. When the computation is done, the output is encrypted (or not, depending on whether the output should be public or private) and then output by the TEE. In this way.
TEEs therefore clearly support the type of single-private-input computations talked about so far. However, the situation is a bit complicated if we want to receive inputs from multiple sources. Indeed, the only way that can be possible, is to make sure the same key is stored on everyone’s TEE.
The last tech I will look at is secure multiparty computation, or MPC. This privacy tech supports both types of computations, just like FHE and ZKPs, but where it distinguishes itself is that it naturally supports private inputs from multiple sources. Indeed, there’s a reason it’s called secure multiparty computation.
This makes MPC especially suited for a blockchain because of its multi-user nature.
The above categorization leaves out a lot of details, since it talked about neither the security models that each of the technologies use, nor about their efficiency.
Each of the four technologies above operate in a particular security model, and none of the models are exactly the same. Likewise, they each have some properties that make them desirable compared to the others. (For example, FHE requires more computation, but less communication, than MPC.)
In general, MPC does seem to come out on top, and is the only technology that easily supports computations where multiple users provide inputs. MPC, by its nature, is a decentralized technology, which is probably why it works so well in a blockchain setting. That being said, an ideal world would probably use all of the technologies in a carefully created orchestration to ensure the best guarantees in terms of both security and efficiency.
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Self-sovereign identity (SSI) is an ever increasingly important concept to enable users control over their own data and let them share it with whom they want. Today, data rests in centralized databases that belong to big enterprises with little transparency into how the data is actually being used and for what purpose.
SSI turns this around and data starts with the users, actually resting at users own device at first. Then it is up to the users to choose with whom and what data they share. Additionally, privacy-preserving features, such as selective disclosure and predicates enhance the user to share data without sharing it all or just prove simple facts about the data.
There are many great tools and infrastructures that can handle SSI, and Partisia Blockchain’s MPC technology adds a new component to the stack that enables new business models, enhances privacy for the data-driven economy, and will take your project ahead of the competition. So read on if you are a builder of the US$27 billion global digital identity market that is expected to expand at a CAGR of 17.2% from 2023 to 2030.
First things first, digital identity usually revolves around three actors: issuer, holder, and verifier.
The issuer issues verifiable credentials to the holder, and the holder can then present the credentials to a verifier who can verify the content by digital signatures and Decentralized Identifiers (DIDs) that may be on a blockchain. For most digital identity use cases, DIDs and associated DID documents are the only elements that get on the blockchain. We do not take a deep dive on this in this article.
DIDs and verifiable credentials are some of the essential components that make up digital identity, especially digital identity that works with decentralized networks. DIDs are a type of address that is generated to manage digital signatures, and verifiable credentials are credentials created and issued by any issuer based on their DIDs.
To enable real SSI, the users will have to store all data themselves at first, often in digital identity wallets, and only then will the user be in full control. The data itself can be data inputs from users such as personal Identifiable Information (PII) or digital verifiable credentials issued by a third-party, e.g. KYC provider issues KYC claim as digital verifiable credential. Credentials are often issued and exchanged by an agency that establishes secure peer wise connections.
Multiparty computation (MPC) is a groundbreaking technology that allows multiple data inputs to remain private while still being computed on and only sharing the outputs. The computing itself is carried out by specially selected MPC validator nodes who each compute on secret shares of the data and privacy is guaranteed by cryptography.
Compared to ZK proofs, such as zk-SNARKs, MPC is a game changer that allows computing on any function. This takes digital identity to the next level because it is now not only possible to share data with privacy features, but also carry out decentralized computation on private data and write business logic into private and public smart contracts to orchestrate the process and rules.
As we learned before, ZK proofs are good for simple presentations about specific data, e.g. a verifiable credential issued by an employer can be used to prove to the bank that you earn more than US$80,000 a year to qualify for a loan without revealing the exact amount you earn.
Now imagine that we need to compute statistics on multiple inputs from multiple users and compare a single person’s salary to the average, all while preserving privacy. ZK proofs cannot handle general computations on multiple inputs and comparison is limited to two users presenting against each other, so another system would have to support it. This is where Partisia Blockchain’s MPC comes to save the day! MPC on Partisia Blockchain can handle multiple inputs and preserve the privacy while carrying out efficient general computation.
Even though all smart contracts and data can be private, it is often worth considering only to push the most sensitive data and operations into private computation because it is generally more expensive than public computation. This goes for all ZK technology. For instance, if you want to calculate the average salary of employees, you might consider just the salary as private inputs plus pseudonymized identity, and then do statistical calculations in the public space.
When we look at DID/SSI solutions, the business requirements of the implementation usually go past simple verification of ID. DID/SSI proof is just the first step. The real challenge is what other data do you need after the verification. Perhaps it is to verify that this person has proper credentials for accessing a system. Or another popular use case for DID is to verify a user has enough assets to pay for something without revealing their total asset holding. Another app that is looking to build on our system is trying to create a persona on-chain, which advertisers can target, without revealing personal information about the user themselves.
In all these use cases, a simple proof system becomes too expensive and slow due to the fact that each individual parameter must require a proof. When you have 10 users, maybe this is possible. But what happens when you need to scale to 1000 or 10,000 users? And proofs are not computations. It is unable to compute the various different private data for analysis.
This is where MPC can extend the functionality of DID/SSI to create multi-functional applications. Through MPC you can both prove and compute multiple parameters in a single computation and include all the additional business requirements while keeping the data private.
During the pandemic, many attempts were made to create a Covid-19 passport so citizens could prove they were either vaccinated or tested negative while preserving privacy. Zk proofs are good for this, but limited to only presenting yes/no results to a verifier without extensive physical verification such as ID cards, which would compromise SSI principles.
In collaboration with HES-SO Valais-Wallis, Partisia Blockchain developed a solution where identification is reduced to matching an individual’s face with an image of the person’s face powered by MPC in order to increase security and privacy. The Partisia Blockchain ensures trustworthy information is broadcasted to the verifier and MPC ensures that the private information about the citizen is used only for matching and kept hidden for the verifier.
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Partisia Blockchain Foundation is happy to announce our grants program for the second half of 2023. We are giving out up to 3 million $MPC tokens (valued at US$1.2 million from last years public sale price of 40¢ a token) to builders that want to create something unique in the blockchain space. Something that is not possible on any other chain.
Partisia Blockchain is the worlds first blockchain that combines a generic programing language to enable a customizable secure multiparty computation (sMPC) solution into an interoperable and scalable blockchain.
Unlike other privacy blockchains that do zero knowledge proof, or only a specific MPC function, Partisia Blockchain’s research based sMPC allows for customizable solutions to fit your specific needs. With features that allow for solutions to be regulatory compliant (such as GDPR, HIPAA, etc), and fully auditable, Partisia Blockchain allows for the solution to solve problems in many use cases.
Our ecosystem is full of unique real world projects that are unique in the blockchain space. From solving for MEV attacks, tackling tender corruption in the public domain, to meeting CSRD compliancy, privatizing DAO voting to ensure integrity of the vote, our partners are not building yet another same type of application in other blockchains. And this is where we want you to come in to build something unique, something not seen in the industry.
Our grant guidelines are here, but mainly we are looking for teams that really want to create a unique solution that cannot be solved in other blockchains. We provide the infrastructure and the technology to bring these solutions to life. We are looking for teams that are really looking to stand out from other dApps, or solve for a problem that is currently not possible in other blockchains.
If you already have an existing app, you dont have to port your existing app over into our blockchain to take advantage of our MPC tchnology. You can use our MPC-as-a-Service model to request the computation as a service. Our interoperability model allows for other tokens (Eth, BNB, Polygon USDC, with more to come) to be spendable as gas on our chain, allowing flexibility to existing applications to take advantage of our technology as well.
What is your unique idea? What are some of the problems you have not been able to solve in the blockchain space? Do you want to create something unique to stand out from all the other similar dApps that do the same thing?
Lets build something different together!
Stay updated:
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Scalability has been something that architects have been grappling with for many years. In the technology space, being able to scale both vertically (adding capacity) and horizontally (adding instances) has been grappled with in all parts of the technology stack. Do you add more transistors in a CPU or add cores to work together? Do you add more space in a hard drive or add multiple hard drives to work together? Do you build a faster computer, or create a software architecture to use multiple computers in sync?
One thing is for certain however. Scaling vertically has limits. There is only so much CPU, memory, storage you can add to a single system before it runs out of capacity.
Blockchain space is no exception. As adoption grows, so must the ability for a blockchain to handle the additional transaction on chain as a result. And in the blockchain space, we call it TPS (transactions per second) This is a combination of two metrics;
Different blockchains use different architectures to try and achieve faster throughput. For finalization, there are things like probabilistic and deterministic finalization. To achieve a higher number of transactions, blockchains have turned to sharding, and added on different rollup technologies like ZK or optimistic roll ups.
To create the fastest blockchain, you have to first look at what can theoretically be achieved for the above two factors. For the finalization time, the fastest is instant. As soon as the block is created, you want it to be finalized as fast as possible. For the number of transactions in a block, you want to be able to put the number of transactions in a blockchain can handle to be as much as possible. The more transactions you can process at the same time, the faster your chain will be. And all of this needs to be done in a secure manner. So how does Partisia Blockchain handle these two challenges?
For finalization, PBC has implemented a unique consensus model that consists of three parts.
This unique finalization model allows for blocks to be created and finalized in real time with the only limit being the time it takes for the verification signatures to propagate throughout the network. (More info in PBC’s yellow paper section 3.1)
While the term “sharding” has been popularized by blockchain, its actually a term that was coined back in the 1990s, by an online video game company, of all places. During the initial popularization of MMORPG (massively multiplayer online role-playing games) the company building the game Ultima Online ran into a scalability problem. To solve the problem of scaling out huge worlds for hundreds of thousands of users to interact with, they came up with a database scaling architecture and coined it “Sharding” This sharding architecture caught on and is now actively being used by many different database products, including MySQL, Oracle DB and MSSQL.
Blockchain has borrowed the term but if you look at the general architecture of most blockchains, it does not conform to the general principles of what sharding really means. In blockchain sharding, while blocks may get created in parallel, it still gets appended to the end of a single chain.
Through “speed of light” finalization we tackled the issue of creating finalization instantly. For the transaction per block issue, we looked to architect the sharding model according to the definition of what sharding really is; True parallel processing of data.
Just like how it is in traditional databases, in Partisia Blockchain, each shard is an independent blockchain. And each shard ,or blockchain, is capable of independently creating, validating and confirming a block. This architecture goes back to the original definition of what a shard is and allows for true parallel processing of blocks. And in the event congestion is detected, the system automatically creates a new shard adding additional capacity dynamically. Through this dynamic scalability architecture, Partisia Blockchain can theoretically scale infinitely, only limited by the number of nodes in the blockchain.
By creating a programming language that allows for developers to use MPC in a generic way, Partisia Blockchain Foundation has made the creation of applications that can harness the power of MPC for different use cases a possibility. Partisia has been at the forefront of providing private MPC solutions since 2008. And by layering this technology on top of an interoperable and scalable blockchain, Partisia Blockchain is paving the way for anyone to create blockchain solutions that can balance privacy and transparency to build trust and ensure integrity.
To learn more about different use cases or partner with us for solutions, please visit partisiablockchain.com, check out our Medium articles, development documentations or email us at build@partisiablockchain.com.
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Have you ever taken a trip to an amusement park? Then you are probably familiar with “amusement park dollars”. The park encouraging you to exchange your native currency to “amusement park” dollars because the only thing that is accepted in that amusement park is the currency of the amusement park. And of course, those amusement park dollars are not good anywhere else except in that park.
This is similar to how the public blockchain industry’s tokenomics works. If you want to play in the Solana ecosystem, you have to have the SOL token. Same with Cardano, where you need to pay using ADA. Theta is TFUEL, etc. The entire ecosystem model revolves around their specific currency.
And like amusement parks, every blockchain is in competition with each other. “We’re cheaper. We’re faster. We’re the easiest to develop on.” So on and so on…
In fact this “competition with each other” scenario has been seen throughout history. And it’s quite interesting to see, historically, who has been the winners in these types of competitions. VHS vs Beta in the 70’s, The desktop wars in the 80s, Ethernet vs Token Ring in the 90’s, search engine wars in the 2000’s, and the streaming war that is currently ongoing. And in almost all cases, the winners in these “wars” was the one who was collaborating rather than competing with others.
So the big question is….. Who is going to win the L1 public blockchain wars?
As mentioned above, the current state of the public “blockchain wars” is all about competing with everyone. The combination of every chain saying they are faster and cheaper, with the silo’ed tokenomic model of each chain forcing users to spend only in their currency locks every dApp in their own ecosystem. This is why interoperability has become one of the biggest topics in the industry.
But can we do it differently?
One of Partisia Blockchains core principles is interoperability. This is because our vision is to enable anyone to create solutions that help establish trust and foster collaboration and this means having an architecture that supports interoperability.
So in this regard Partisia Blockchain created a platform from scratch. And following the vision and principles we are adhering to, we created the concept of Bring Your Own Coin (BYOC).
BYOC basically means the users of the chain can pay for using apps developed on PBC using the coin they are most comfortable with. Or in other words, the gas payment on our chain is other liquid coins. This allows for the following possible features.
The Hermes bridge is a double-entry bookkeeping system securing the bridged asset through our MPC multi-sig oracle key. Currently supporting Ethereum, BNB and Polygon USDC, our roadmap includes others like bitcoin, ADA, XTZ and allows for simple integration to all other EVM compatible tokens. This interoperability and gas payment model opens up a variety of interesting use cases, such as the ability for users to interact with any dApp using their own currency of choice.
Our MPC-as-a-Service is also a unique feature of Partisia Blockchain. Our core vision is empowering anyone to be able to utilize our MPC services and to achieve this vision, we designed an architecture that allows anyone to call the blockchain, regardless of where their core app is built. Whether it is a traditional Web2 or a Web3 application that is built on a different chain, both can call Partisia Blockchain and compute using secret inputs without needing to port their entire application stack over to Partisia Blockchain.
By creating a programming language that allows for developers to use MPC in a generic way, and combining it with a unique interoperability and a scalability architecture, Partisia Blockchain Foundation has made the creation of applications that can harness the power of MPC for different use cases a possibility. Partisia has been at the forefront of providing private MPC solutions since 2008. And by layering this technology on top of an interoperable and scalable blockchain, Partisia Blockchain is now paving the way for anyone to create solutions that can balance privacy and transparency to build trust.
To learn more about different use cases or partner with us for solutions, please visit partisiablockchain.com, check out our Medium articles, development documentations or email us at build@partisiablockchain.com.
Guest blog by Parker Duncan and Giorgio Guidett, MetaNames Co-Founders.
MetaNames is a decentralized Domain Name System (DNS) built on top of the Partisia blockchain. MetaNames enables users to create human-readable domain names that are linked to Partisia addresses, smart contracts, user socials and IPFS content.
MetaNames simplifies the process of interacting with the blockchain. Instead of using long, complex hexadecimal addresses for transactions, MetaNames allows users to use short and easily recognizable domain names, just like traditional domain names on the internet.
The key benefits of MetaNames include:
Moreover, MetaNames brings fresh air to the NS ecosystem by leveraging Partisia features such as privacy-preserving contracts and BYOC logic:
Check out our roadmap below:
MetaNames aims not just to provide a base and core chain infrastructure but to fully leverage Partisia Blockchain’s innovative technology to improve the current NS industry.
Stay tuned for more, and keep up to date with us by following our Twitter!