Trust, the cornerstone of any relationship, holds profound significance in our interconnected world. Whether it be interpersonal, inter-corporate, or even between governments, trust is the linchpin upon which successful relationships are built. Partisia Blockchain, in its essence, is a catalyst for redefining trust in the digital realm.
“The art of enabling trust within an inherently untrusting environment.”
But how does Partisia Blockchain achieve this monumental feat?
The importance of trust is a concept we can all readily grasp. Yet, the question arises – what facilitates trust among entities? A critical factor in this equation is privacy. Trust cannot manifest in a realm of complete anonymity. However, it also cannot thrive in a domain of absolute transparency. The sweet spot lies in a harmonious blend of both, where transparency and privacy coexist to cultivate trust.
Consider a simple scenario: applying for a credit card. A bank, to grant you this privilege, requires crucial information about you – your identity, income, contact details, and credit history. These details are essential for the bank to extend its trust in you. However, if you knew that these details would be made public, including your transaction history, your willingness to apply for that credit card would understandably wane.
Upon closer examination, it becomes clear that privacy is the very bedrock upon which trust is constructed.
In our credit card example, you enter a trusted environment where you bestow your trust upon the credit card company to safeguard your personal data. The company is, of course, bound by its business model to keep the sanctity of this trust. However, there is an underlying vulnerability here – data breaches, unauthorized sales, or inadvertent data exposure. These are potential avenues through which your trust can be shattered. In such cases, you are placing trust in the environment.
On the other hand, a trustless environment requires no implicit trust in any single entity but still manages to perform necessary functions seamlessly. Enter the decentralized blockchain, a prime example of an environment where trust in an entity is unnecessary. Here, blocks are formed, and information is inscribed on the ledger without a single entity having dominion over the system. The laws of physics exemplify another trustless environment – they exist without needing trust because the system is transparent, having been measured and documented, and accessible to all for independent confirmation.
Partisia Blockchain ingeniously fuses these concepts to tackle the trust predicament. It enables two entities to work together within a trusted framework, devoid of any single entity’s control. The synergy of multiparty computation (MPC) and blockchain technology underpins this innovation.
MPC, a revolutionary technology, permits the computation of confidential data. It functions as an encryption technology, allowing input values to remain hidden while still computing answers. It empowers us to calculate the sum of 2 and 3 without revealing the original values. Envisaged in the late 1970s and officially introduced in 1988, MPC boasts an illustrious history, with over a thousand research papers and 15 years of practical implementation.
In parallel, blockchain, the other keystone of Partisia Blockchain, has been redefined to its very core. Through our distinctive BYOC tokenomics principles and an infinitely scalable architecture, we make trust in a trustless environment a reality for all – not just for those building on our platform.
By converging these two technologies, Partisia Blockchain democratizes trust within a trustless domain. This manifests as a wealth of opportunities: companies collaborating without exposing their confidential data, creating equitable, private, and accurate voting systems, returning control of data to users, and enabling monetization of data. In the heart of Partisia Blockchain’s mission lies the potential to revolutionize business models and unveil novel value propositions.
In today’s context, the healthcare sector by itself contributes to around 30% of the global data volume, while the pharmaceutical industry significantly adds to this data generation. Handling and utilizing data from these sectors are also subject to some of the strictest regulations due to the nature of data that often includes personally identifiable information. GDPR, internal policies, and other regulatory frameworks pose tough challenges when data is collected or shared beyond isolated data silos for analytical purposes.
Public and private blockchains serve as effective tools for maintaining an immutable and transparent log of transactions, which can be relied upon and examined by various stakeholders such as public authorities. However, when it comes to the actual manipulation and processing data, both public permissionless blockchains and private blockchains are insufficient due to the lack of privacy features. This is where Partisia Blockchains’ distinctive and proprietary secure multiparty computation (MPC) technology emerges as exceptionally valuable
Our MPC technology empowers individuals and organizations to preserve privacy right from the input stage. This entails breaking down data into many encrypted secrets, which are then shared with specialized MPC network nodes. Critically, these nodes remain unaware of the specific content they store or compute on. Predetermined private and public smart contracts establish protocols for computations and determine access privileges to the outcomes, as authorized by permissions.
The potential applications for private computations within the healthcare and pharmaceutical sectors are virtually limitless. In this article, we will explore some of the extensively discussed scenarios.
Privacy technologies play a pivotal role in enhancing the security and confidentiality of private DNA sequencing. With the advancements of genetic analysis techniques, individuals are increasingly seeking to unlock insights from their genomic data, but the sensitive nature of genetic information demands robust measures to preserve privacy. MPC offers solutions by enabling private computations on encrypted genetic data without the need to expose the raw data. This allows for collaborative research, personalized medical insights, and genetic advancements while ensuring that individuals retain control over their sensitive genetic details.
By employing these technologies, private DNA sequencing initiatives can preserve privacy, encourage data sharing for scientific progress, and mitigate the risks associated with unauthorized access or breaches of genetic information.
Traditional data sharing approaches often raise concerns about privacy breaches and data ownership when it comes to the almost abundant amount of sensitive patient information and proprietary research data for healthcare and pharmaceuticals. MPC addresses these challenges by allowing multiple parties to jointly analyze and derive insights from their respective datasets without actually revealing the raw data to each other, but only share valuable outputs.
In the context of clinical research, pharmaceutical companies and healthcare institutions can collaboratively conduct analyses on aggregated datasets while keeping individual patient information and proprietary data secret. This facilitates cross-institutional research without the need to centrally consolidate data, eliminating the risks of data exposure and unauthorized access. Different pharmaceutical companies, each possessing valuable proprietary data, can engage in joint studies without revealing their confidential insights.
This collaborative approach unlocks opportunities for discovering broader trends, identifying potential drug interactions, and conducting large-scale analyses that draw from diverse datasets. By preserving privacy and ownership, MPC encourages cooperation among entities that might have otherwise hesitated due to privacy concerns. In essence, MPC bridges the gap between robust data-driven insights and the need for privacy, fostering a new era of collaborative clinical research across previously isolated data silos and organizations.
MPC offers robust primitives to revolutionize supply chain management within the pharmaceutical and healthcare industries. In these sectors, ensuring the integrity, transparency, and security of the supply chain is of all importance, as any inefficiencies or vulnerabilities can have serious consequences for patient safety and product quality.
MPC provides a solution by enabling various stakeholders, including manufacturers, distributors, regulatory bodies, and even healthcare providers, to collaboratively manage the supply chain without revealing sensitive proprietary information to one another. This is particularly valuable when dealing with complex global supply networks involving multiple parties, each with their own data and interests. Parties can jointly verify and validate critical supply chain information, such as the authenticity of raw materials, production processes, transportation routes, and inventory levels.
For example, pharmaceutical companies can verify the authenticity and quality of raw materials supplied by third-party vendors without sharing their precise formulation details. Regulatory agencies can conduct audits and ensure compliance across the supply chain while preserving the confidentiality of manufacturing processes. Healthcare providers can track the provenance of medical devices or drugs to enhance patient safety and prevent counterfeiting.
MPC-driven supply chain management ensures trust among stakeholders by providing a secure environment for collaboration. It prevents fraud, minimizes the risk of data breaches, and streamlines information sharing. By harnessing the power of MPC, the pharmaceutical and healthcare industries can establish a more efficient, transparent, and secure supply chain ecosystem that ultimately benefits patients, regulatory compliance, and business operations alike.
MPC presents a transformative way for streamlining the recruitment process in clinical trials while upholding patient privacy and data security. Clinical trial recruitment often entails the sharing of sensitive patient information across multiple stakeholders, including healthcare providers, research institutions, and pharmaceutical companies. MPC offers an innovative approach by allowing these entities to collaboratively identify eligible participants without revealing individual patient details.
Using MPC, each participant contributes encrypted data, maintaining the confidentiality of their personal information. The parties can collectively perform computations on this encrypted data to match potential participants with specific trial criteria, such as medical history, demographic characteristics, or genetic markers. This process ensures that no party gains access to the raw data of others, mitigating privacy concerns.
MPC technology not only accelerates the participant matching process but also encourages broader collaboration among stakeholders who might otherwise hesitate to share sensitive patient data. This approach streamlines the recruitment process, reduces administrative burden, and respects patients’ privacy rights. Ultimately, MPC revolutionizes clinical trial recruitment by combining efficiency and data security, fostering trust among stakeholders and contributing to the advancement of medical research.
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Changing the data market business model from buying and selling of your data to buying and selling the “use” of your data.
Current advertising data market industry involves selling and buying of data. Regardless of the type of data the advertisers are looking for, it’s all about collecting the data from various means, categorizing it, perhaps pseudo anonymizing it and selling the data to advertisers. And data, as it turns out, is a very lucrative business. The global market size of the advertising market is estimated to be US$600–800 billion and the internet makes up about half of that size.
You probably have heard this statement before. If it is free, you are probably the product being sold. And this is a very common way for the data market players to create a “free” application that allows the collection of data that the market players will buy and sell. The more accurate the data, the more valuable. Google, Facebook, Twitter, Microsoft, etc all use similar business models. But there are other players in this market, some you may have heard of in the news (Cambridge Analytics for example) or smaller companies that trade your data under the covers. They will collect from various sources, reshuffle, and resell the data to others.
But as with any business model, there are challenges and the data market is not without its share of issues.
Partisia Blockchain’s privacy first blockchain with research lead secure multiparty computation (sMPC) can help solve these issues and also provide data market participants with alternate business models that can bridge the gap between consumer privacy concerns and better data overall.
This changes the data market business model from buying and selling of your data to buying and selling the use of your data. By shifting the paradigm to a services model, new potential revenue streams become available while being able to solve some of the difficult challenges facing the advertising industry.
Projects like Blockchain-Ads and Kin are already looking to take advantage of this new model and we are exited to see where this will lead in the future.
Connect with us at build@partisiablockchain.com to see how we can help you create new business models, solve challenges and provide new incentives for the users to use your system.
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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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At present, it requires a fair amount of technology knowledge to build and support a node in PBC. While many who do not have a technology background have been able to build and maintain nodes, it still creates a barrier that many individuals or organizations feel hesitant to cross. The other challenge is the current staking and job association process. Because the staked MPC tokens in a node are being used as collateral for all the types of jobs the node is running, unstaking and unassociating tokens that are being used can be a challenge.
This is why we are putting focus on implementing a simpler node setup and operations process. This will allow easier setup of nodes as well as easier associating and dissociating of your tokens.
These new features will be rolled out in phases, with the first one being automatic node registration. A simplified registration process will be rolled out where just the configs on your node will kickstart the KYC/KYB and the registration process.
Second phase of the project will be to simplify the association and dissociation of your staked tokens. It will all be a part of our “Staking 2.0” model, which will look to make it easier and less restrictive for both node operators and delegated stakers to manage their stakes.
Running a node will still require some level of technical skill. We hope by automating some of the process, it will make it less confusing and easier for the registration process.
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As we continue to see development occurring on our blockchain, we are always on the lookout for feedback on how to improve our platform, either to make it easier for developers to develop on, or to add new functionalities to help improve the product offering by the teams developing on our chain.
In the last 6 months we’ve been listening to the developers and have been working on various new functionalities and features that should help improve the quality and functionality of the projects. Some of them came in the form of development tools which we reviewed last week.
In this article, we will review the new feature sets that are upcoming to help developers improve upon the existing features or add new functionalities.
Some of these features will also create new use cases for the MPC tokens
Improvements in the PBC as a Second Layer Functionality
One of our main value propositions is our cross-chain bridge (Hermes). This unique bridge and gas payment system allows other chains to be a usable asset in Partisia Blockchain. It is this bridge that allows other chains to call our MPC technology as a service and pay for it using their native coins. But at the moment the data that is transferred is through manual data attestation, and the transfer only occurs in one way (PBC -> EVM).
By implementing BYOC messages to be supportable, we will now implement a two way communication system. This allows for the smart contract writer to hook events that are happening in the EVM back to PBC. Through adding a MPC powered threshold key and tying our oracle servers to collateralize these data through MPC tokens, developers will now be able to create an automated bidirectional flow of data between the EVM and PBC. This opens up additional functionality, such as adding message information in NFTs and allowing them to be transferable cross chain, or sending and receiving of data during the actual bridging transfers.
Staking as an Insurance
Currently, once the funds inside a smart contract runs out, the contract gets deleted. We will be creating a new method to allow for SC owners to stake their MPC tokens as insurance to allow for these smart contracts to continue to live on even if the funds run out of the contract. This will allow for both a safety mechanism in the event funds do run out and also introduce another use for MPC tokens.
ZK Contract Lifetime Beyond 28 Days
Currently the data in a ZK contract lasts for 28 days. Going forward, we will introduce a way to allow for this data to be extendable to go past the 28 days through staking MPC tokens and re-funding the smart contract.
ZK Computation in Batches
This feature will allow for multiple ZK computations to be batchable so that you do not have to execute each and every computation one at a time.
Allow ZK Contracts to Control User Data
Currently the developers writing ZK smart contracts do not have flexible control of the Zero Knowledge data. Through this feature, we will allow for developers to have greater control of the type of data and the control of how the data will be used.
We hope that the above will both help improve the speed in which development can be done as well as allow for new features to be implementable by the developers.
As each of the items complete, we will make a separate announcement in our development channels.
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