🎉 Gate xStocks 交易開啓啦,現貨、合約、Alpha齊上線!
📝 在Gate廣場發帖,曬出你的交易體驗或精彩截圖,瓜分$1,000大獎池!
🎁 廣場優質創作者5名,每人獨享$100合約體驗券!
🎉 帖文同步分享到X(推特),瀏覽量前十再得$50獎勵!
參與方式:
1️⃣ 關注 @Gate廣場_Official
2️⃣ 帶 #Gate xStocks 交易体验# ,原創發帖(不少於20字,僅用活動標籤)
3️⃣ 若分享到推特,請將連結提交表單:https://www.gate.com/questionnaire/6854
注:表單可多次提交,發布更多帖文可提升獲獎機會!
📅 7月3日16:00—7月9日24:00(UTC+8)
詳情:https://www.gate.com/announcements/article/45926
每一條體驗,都有機會贏取大獎!快在Gate廣場show出你的操作吧!
MPC and Blockchain Cryptography: A Deep Dive
Multiparty computation (MPC) and blockchain are both distributed systems. This article serves as a learning note (a directory entry) on cryptography, MPC, and blockchain.
Symmetric encryption, asymmetric encryption, and hash functions basically constitute all levels of cryptographic algorithms.
Cryptographic protocols include:
A key concept in MPC is simulation, which means bringing the hypothetical world of the ideal into the real world (especially with regard to trust). Six important characteristics that need to be considered are:
Based on different levels of untrustworthiness, there are several security models in cryptography:
Garbled circuits (GC) and oblivious transfer (OT) are two protocols that are suitable for logical operations but not arithmetic operations. They are mostly studied between two parties rather than multiple parties (although many people are researching how to convert 2PC into MPC).
The bottom-level technology of MPC is secret sharing (SS), which essentially involves taking some points to fit a curve. It is more suitable for linear operations such as addition, but not for multiplication. Multiplication is also a major challenge when constructing MPC. SS has advanced to include verifiable secret sharing (VSS) and publicly verifiable secret sharing (pVSS). pVSS can be understood from a cryptographic perspective as a blockchain, but there is currently no mature solution.
The difficulty of completing digital signatures with MPC lies in how to use TSS to complete an asymmetric encryption.
Digital signatures:
In summary, MPC is a cryptographic technique that enables multiple parties to collaborate on a computation without revealing their private inputs. It is a crucial tool for achieving privacy-preserving distributed computation and can be applied to many fields, such as secure machine learning, secure computation outsourcing, and privacy-preserving data analysis. Note: this article is a translation and summary for easier reading of a Twitter thread by 0x30cF.