ដំណោះស្រាយការគណនាពហុភាគី (MPC)៖ តើអ្នកប្រើប្រាស់បានល្អបំផុតដោយរបៀបណា?

Multi-Party Computation (MPC) is a technology that enables secure data processing and sharing between multiple parties with no single party having access to the full set of data.

This type of distributed computing has been gaining traction in recent years, as its utility includes securely performing computations on personally identifiable information (PII), without the participants accessing the raw data. To ensure that no single participant has access to all the data, cryptologists have developed various protocols which enable parties to split and share encrypted pieces of data among themselves.

What is Multi-Party Computation?

At its core, MPC is a technology that allows multiple parties to compute data with no single party having access to the raw data. They accomplished this by splitting the data into pieces and encrypting them so that no participant can decrypt it on their own.

A key component of MPC is that it allows for computation on encrypted data, so participants cannot see what the other parties are performing computations on or what results they are getting out of the process.

History of MPC

Multi-party computation (MPC) first made a splash in the 1970s, when Chinese cryptography legend Andrew Yao created the Garbled Circuits Protocol, which allowed two parties to compute data without revealing their inputs. His Millionaires’ Problem gave a simple example of an MPC two-party system.

In 1987, the GMW (Goldreich–Micali–Wigderson) protocol was born, allowing for truly multi-party platforms, and in 2008 MPC had its real-world debut in a Danish sugar beet sealed-bid auction that preserved the privacy of all bidders involved. This marked the beginning of a revolutionary new way to conduct secure digital transactions with multiple participants.

How Does Multi-Party Computation Work?

MPC uses cryptography techniques such as secret sharing and homomorphic encryption in order to split up and share encrypted pieces of data between multiple parties. Secret sharing involves splitting a piece of information into several components, with each party only receiving one piece, meaning none of them to have access to the full data. Homomorphic encryption is used to enable computations on encrypted data, meaning that they don’t expose sensitive information in plaintext form.

An example to illustrate how Multi-Party Computation Works

Let’s say three companies, A, B, and C, want to collaborate on a project but don’t trust each other enough to share their sensitive data. By using MPC solutions, they can securely split up the data among themselves and perform computations on it, with none of them having access to the raw information.

First, A, B, and C will use secret sharing algorithms to split up their data into several components. Each company will then encrypt these pieces using homomorphic encryption algorithms and send them to the other two participants. Now, all three parties have encrypted pieces of data from each other, but none of them can decrypt it on their own and access the full set of information.

Next, A, B, and C can perform computations on the encrypted data without ever having to decrypt it. This means that each participant can only see their own contributions, while still being able to collaborate on the project. Finally, since none of these participants have access to the raw data of each other, they can be sure that their own information is secure.

Why is MPC is called privacy-preserving computation?

Data is an irreplaceable tool in today’s world, with many of the world’s most revolutionary and progressive advancements directly traceable to it. But data sharing all too often comes with incalculable risks of privacy breaches or even loss of control.

Multi-Party Computation (MPC) offers a creative solution to this issue, helping to create a new online atmosphere where parties can access certain types of data without compromising the safety of other persons’ information or their own.

MPC uses secure algorithms that don’t expose any data except for the results, meaning parties can make important decisions without revealing personal details or violating others’ privacy rights. This technology could revolutionize data security as we know it and pave the way for a secure future filled with opportunities stemming from helpful information sharing.

Benefits of Multi-Party Computation Solutions

MPC solutions offer a wide range of benefits, including:

• Increased security – By splitting up encrypted pieces of data and not exposing any raw data at any point, MPC ensures that no single party can access all the information. This makes it an ideal solution for processing highly sensitive information, such as PII or medical records.

• Improved privacy – As each participant only receives part of the overall data set and no single party has access to all the information, MPC also helps improve privacy by preventing any one party from profiling individuals.

• Enhanced speed and scalability – MPC solutions can run computations in parallel, meaning that they are able can process large amounts of data quickly. This is especially beneficial for tasks such as machine learning, which require lots of computational power to perform.

Disadvantages of Multi-Party Computation Solutions

The major disadvantages of MPC solutions include:

• Higher costs – Implementing and running an MPC solution requires more resources than traditional computing techniques. This includes having to purchase the hardware, software, and other tools needed for the setup.

• Complexity – Setting up an MPC system can be complex because of the additional cryptography techniques needed. This can also make it difficult to troubleshoot and debug, as any issues need to be addressed across multiple parties.

• Slow speeds – Since MPC solutions are running computations on encrypted data, they can often run slower than traditional computing processes. This means that tasks requiring large amounts of computational power may take longer to complete.

MPC Applications in the real world

ការធ្វើតេស្តហ្សែន

Geneticists use MPC to analyze genetic data. Instead of sending raw DNA sequences over the internet, each party encrypts their own data and sends it to a third-party server where MPC can compare, analyze, and interpret the results without having all parties reveal their individual information.

ប្រតិបត្តិការហិរញ្ញវត្ថុ

You can use MPC to secure financial transactions. You can achieve this by splitting the data into multiple pieces and processing it in a secure MPC environment, ensuring that no single party has access to all the information. This makes it ideal for digital payment solutions such as cryptocurrency exchanges, where privacy is of utmost importance.

ការស្រាវជ្រាវវេជ្ជសាស្ត្រ

You can use MPC solutions to share and analyze large amounts of medical data. By encrypting the data before sending it, each party can access certain information compromising no other person’s privacy or security. This makes MPC an ideal solution for clinical trials and other research projects involving sensitive patient data.

Threshold signing in blockchains

MPC can protect digital signatures in various blockchain projects. They achieved this by splitting the signature among multiple participants, making it so that no single party has access to the entire signature. This ensures that digital signatures remain secure and tamper-proof even if one party gets compromised.

Secure alternatives to MPC

Cryptographic methods

Cryptographic methods are an integral part of computer security that allows us to store and transmit sensitive data securely. Two of the main cryptographic methods used for this purpose are homomorphic encryption and zero-knowledge proofs.

Homomorphic encryption uses mathematical formulas to enable the computation of encrypted data without decrypting it first, making it easier to share data securely without compromising privacy.

Zero-knowledge proofs provide mathematical techniques to verify the truth about information without revealing its detail, making them extremely useful when dealing with confidential information.

Another technique used in cryptography is differential privacy, which adds a controlled amount of randomness to the collected data, preventing malicious parties from obtaining users’ personal details. Essentially, cryptographic methods offer us more control over our data by providing an increased layer of security and protection against data breaches.

AI/ML-backed methods

AI/ML-backed methods are helping to power the next generation of privacy-driven initiatives. Two key techniques that are enabling this shift are synthetic data and federated learning.

Synthetic data is a form of artificial intelligence that creates data points that replicate the distribution of relevant characteristics without actually using actual information.

Federated learning is a form of distributed machine learning technique where analysts train models across multiple datasets simultaneously without the risk of compromising any confidential or sensitive information stored in them.

Together, these two methods enable both better accuracy and stronger data privacy protections from start to finish, allowing us to make smarter decisions with greater assurance.

សន្និដ្ឋាន

MPC is an increasingly popular technology enabling secure data processing between multiple parties with no single party having access to the full set of data. It uses cryptographic techniques such as secret sharing and homomorphic encryption to split up and encrypt pieces of data, ensuring that none of the participants can access the raw data or profile any individual from it.

With its many benefits, including increased security, improved privacy, and enhanced speed and scalability, MPC solutions offer a powerful solution for organizations to securely and efficiently process sensitive data.