During the course of July, students, young professionals, academics, and industry leaders came together from across the globe for the SDG Summer School. This Summer School is organized by the University of Geneva and hosted in the SDG Innovation Lab, close to the United Nations as well as on different campuses around the world in parallel. Partisia Blockchain had the privilege of attending and actively participating in the program.
Groups of students were formed, assigned a coach and began to develop an idea into a project. The projects were all aimed at solving pressing problems and contributing to the Sustainable Development Goals (SDGs). After three weeks the groups pitched their projects to a jury consisting of decision makers from major institutions such as the University of Geneva, the Global Fund and the Olympic Committee.
I had the opportunity of representing Partisia Blockchain and coaching a group of students in creating an innovative solution to women’s health:
“Her Menstrual Trials” is an application using MPC and Blockchain to track women’s health trends and the effects of medications without revealing anyone’s personal health data. The blockchain would provide crypto-incentives to the participants and help store and track the data collected, while MPC would keep the data private, while allowing for real-time, continuous data collection from the participants.
Furthermore, Partisia Blockchain had the pleasure of sponsoring a student from Copenhagen to come to Geneva and participate in the Summer School.
The SDG Summer School is an impactful event empowering young minds to find solutions to the most pressing problems around the world. Partisia Blockchain is proud to support such an initiative and help to contribute to a brighter future for all.
Website • Twitter • Discord • Telegram • LinkedIn • Facebook • Instagram • GitLab • Medium • YouTube
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
Website • Twitter • Discord • Telegram • LinkedIn • Facebook • Instagram • GitLab • Medium • YouTube
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.
Website • Twitter • Discord • Telegram • LinkedIn • Facebook • Instagram • GitLab • Medium • YouTube