
 RSK Smart Bitcoin
 #RBTC

RBTC Price:  $73,910   Volume:  $215.7 K  All Time High:  $79,083   Market Cap:  $0.2 B 

Circulating Supply:  2,793 
 Exchanges:  1
 Total Supply:  21,000,000 
 Markets:  4
 Max Supply:  — 
 Pairs:  4


The price of #RBTC today is $73,910 USD.
The lowest RBTC price for this period was $0, the highest was $73,910, and the exact current price of one RBTC crypto coin is $73,910.09359.
The alltime high RBTC coin price was $79,083.
Use our custom price calculator to see the hypothetical price of RBTC with market cap of BTC or other crypto coins. 
The code for RSK Smart Bitcoin is #RBTC.
RSK Smart Bitcoin is 5.9 years old. 
The current market capitalization for RSK Smart Bitcoin is $206,430,891.
RSK Smart Bitcoin is ranked #189 out of all coins, by market cap (and other factors). 
There is a medium daily trading volume on #RBTC.
Today's 24hour trading volume across all exchanges for RSK Smart Bitcoin is $215,688. 
The circulating supply of RBTC is 2,793 coins, which is 0% of the total coin supply.
A highlight of RSK Smart Bitcoin is it's amazingly small supply of coins, as this supports higher prices due to supply and demand in the market. 
RBTC has limited pairings with other cryptocurrencies, but has at least 4 pairings and is listed on at least 1 crypto exchange.
View #RBTC trading pairs and crypto exchanges that currently support #RBTC purchase. 
Merged Mining and Decentralization In this article we analyze the benefits and risks of merged mining, and we highlight the potential of blind merged mining to create a fairer mining market. We begin with an informal presentation of the basics of mining and merged mining, and then delve into mining incentives with the goal of creating a useful taxonomy. Finally we show how blind merged mining protects the Bitcoin mining incentives in the long term. The article was illustrated with images created using Midjourney. — Mining Basics  Mining is a method to protect a blockchain ledger from double spends. A consensus rule establishes that the only way to extend the chain with a new block is to perform an amount of computational work. The amount of work required is proven by a succinct message called proof of work (PoW). The message also contains the block header so the PoW is attached to each block. The miners are the parties that extend the blockchain by working and proving the work of a new block. The amount of work that needs to be proven depends on the current blockchain difficulty which adapts automatically to keep the block rate constant. The blockchain data structure is actually a tree, where miners can potentially extend the tree from any block, creating a new branch. Nakamoto consensus suggests that honest miners should select the branch to extend among all available branches as the one with the highest cumulative work, measured by the accumulated work...
 Building zkSNARKs (volume 3) Introducing QAPs and, subliminally, R1CS. — The author thanks Michael Dziedzic and Unsplash for the image — Introduction  This post closes our series on the construction of zeroknowledge SNARKs. It will revolve around the concepts of QAP and R1CS. In our previous posts, we learnt the basic definitions and properties and also how to build SNARKs to prove knowledge of a polynomial. In this last post, we will learn how to reduce any computation to a polynomial, so one can run the SNARK protocol learnt in post 2. — Quadratic Arithmetic Programs  — Definition. — Up to this point, we have explored how to build zkSNARKS to prove knowledge about a polynomial. Nevertheless, our objective is to prove the integrity of any computation. To do so, we need to convert a program into a polynomial. The tool we need to perform this conversion is the Quadratic Arithmetic Program (QAP), which we present in this section. A Quadratic Arithmetic Program (QAP) over a field 𝔉 is a tuple of polynomials defined on 𝔉[x]: together with a target polynomial t(x) ∈ 𝔉[x]. Let’s assume that F is a function taking n elements in 𝔉 as inputs and outputting n’ elements in 𝔉. Let’s denote N = n + n’ the total number of elements, counting inputs and outputs. We say that a QAP, denoted Q, computes F if (c_1, …, c_N) ∈ 𝔉^N is a valid assignation of inputs and outputs for F, equivalently: if there...
 Building zkSNARKs (volume 2) zkSNARKs for polynomials, step by step. — The author thanks Mak and Unsplash for the image — Introduction  In our previous post, we introduced the concept of SNARK and the basic properties required. With the main objective of this series of posts in mind, namely: how to build zkSNARKs for any computation, we introduce in this post the creation of a SNARK to prove knowledge of a polynomial. We will do it step by step departing from one construction which will not meet all the requirements and ending a protocol which will be a “full” zeroknowledge SNARK. — ZKSNARKs for polynomials  We will build a zkSNARK step by step, starting from a simpler construction which is not a SNARK, and then improving it with new techniques until we meet our objectives. — First step: the Schwartz — Zippel lemma. — We want to create a protocol allowing a prover P to convince a verifier V of the knowledge of a particular polynomial of degree n with coefficients in a finite field F. In other words, P wants to convince V that he knows a polynomial of the form Let us assume that a_1, a_2, … , a_n ∈ F are the n roots of this polynomial, therefore p(x) can be written as Let us assume that V knows d < n roots of the polynomial p(x). Then we can reformulate our problem: now P wants to prove to V that he knows a polynomial h(x) such that The polynomial t(x) will be called the target polynomial. Observ...
 Building zkSNARKs (volume 1) Basic concepts and properties. — The author thanks Benjamin Lehman and Unsplash for the image — Introduction  Welcome to this guide on how to build zkSNARKs! ZeroKnowledge Succinct NonInteractive Argument of Knowledge (zkSNARK) is a cuttingedge technology that provides privacy and security in blockchain systems. It allows for verifying the authenticity of transactions without revealing the details. In this guide, we present an overview of zkSNARKs, their applications, and how to build them. You will also learn about the mathematical background behind zkSNARKs. For example, you’ll learn how to generate the Quadratic Arithmetic Program (QAP) associated with an arithmetic circuit. The QAP is the central object used in zkSNARKs, and its generation is a crucial step in building a zkSNARK. To understand the mathematical concepts behind zkSNARKs you will need a strong background in algebra, particularly fields and groups. Throughout this series of 3 Medium articles, we will also explain the steps to generate a QAP, including representing an arithmetic circuit as a system of linear equations, converting the linear system to a polynomial system, and transforming the polynomial system into a QAP. We hope this guide will help you acquire a solid understanding of zkSNARKs and the skills necessary to generate QAPs by hand. This guide provides the knowledge and tools necessary to build your own zkSNARKs and co...
 Verifiable Homomorphic Encryption The multigroup setting — One scheme to rule them all. — The author thanks Chris Curry and Unsplash for the image — Introduction  Homomorphic Encryption (HE) allows computations on encrypted messages without decryption. While a fully HE scheme supporting arbitrary computations was an open problem, Gentry’s breakthrough led to significant progress, including the development of BFV and CKKS. HE is suitable for cloudbased environments, as it supports secure computation without the data owner’s presence. However, standard HE has limitations in situations involving more than one user, such as the multiparty setting. In the case of multiple data sources, using a singlekey HE leads to an authority concentration issue, as one party gains access to all data. Multiparty HE (MPHE) and multikey HE (MKHE) are examples of schemes that overcome this limitation by distributing the decryption authority among multiple parties, thus protecting the privacy of data owners. One major drawback of implementing HE schemes in cloudbased environments is the absence of any guarantees for clients regarding the accuracy of computations carried out by the cloud. Although there are typically servicelevel agreements between clients and clouds, some clients may require more reliable assurances and errordetection capabilities to guard against untrustworthy cloud providers who could quickly introduce errors in their sensitive data a...
 Optimistic State Prefetching (OSP) A recent paper suggests that the EVM performance can be increased 6x with speculative execution. The idea is to use the information gathered by executing transactions while they are in a node’s mempool to optimistically choose execution paths when the transaction is executed within a block. Vitalik Buterin has questioned this research arguing that this speedup corresponds to the average case, but in the worst case it may be the opposite: a huge slowdown. Attackers can try to create worstcase transactions to delay block processing of competing block producers. While we agree with Vitalik, a new scheme presented in this article enables a comparable speedup (yet to be proven by benchmarks), without the original downsides and with almost no extra complexity. We propose that users creating transactions can specify which storage cells can be prefetched during transaction propagation but instead of giving them explicitly as EIP2930 access list, we let network nodes compute the access list onthefly in the mempool. The users bet that the state addresses accessed will be exactly the same when executing the transaction inside a block. The benefit over access lists is that access lists are costly in terms of bandwidth, so the incentive to provide them is low. We increase the incentive to use implicit access lists. We also add an extra economic incentive not present in EIP2930: we reward senders that can predict the full set of stat...
 EVM blockchain Scalability & ACID Database Properties The main data structure that holds accounts, balances, code and contract storage in an EVMbased blockchain is the world state trie. The efficient management of this data structure is one of the most important topics in scaling these blockchains. How and where the trie is stored, and how fast it can be accessed and modified are crucial parameters of the performance of the network client. If the trie fitted in RAM, then most of the problems would be solved. But it generally does not, so how the trie is persisted is of extreme importance. Ultimately the trie is composed of nodes that are serialized and stored in a database on disk. We’ll call this state database the statebase, for short. Currently the statebase must be stored in Solid State Drives (SSDs) as hard disks are too slow for scattered reads, as usually required by network clients. During the last years, network clients improved as the block gas limit was increased, but improving the statebase has been challenging. The statebase is currently the main EVM performance bottleneck. For example, the Erigon/TurboGeth team switched statebases 3 times to improve performance. A new statebase (LMPT) was also designed to tackle this problem. The problem has been also tackled from the protocol standpoint. Accesslists (EIP2930) enable prefetching statebase data to reduce later stress during block execution. EVM state I/O opcodes were repriced several times to account for state...
 Client Performance with Experimental Storage Rent Implementation Our proposal for Storage Rent in Rootstock (RSKIP240) adds a new field to each node in the RSK Unitrie. The new field, called lastRentPaidTime, is a unix timestamp indicating the last time storage rent was collected for that trie node. Our current implementation introduces new classes of Java objects — e.g. StorageRentManager, Rented Node. These are used for rent tracking, rent computation, and rent timestamp updates — i.e. actions are triggered by trie access (reads, writes, deletes) methods in the Repository. Naturally, these changes will have some impact on the performance of RSKj nodes, e.g. block execution time, disk IO access, disk usage etc. So, we conducted some experiments to measure such impact. — What we expected  We expect storage rent to utilize more RAM and consume more disk space — this is because of the new timestamp adds 8 bytes to each unitrie node. We also expect the rent implementation to run a bit slower. This is because the node tracking, rent computations, and rent updates should lead to longer block execution time and more disk IO. — What we found  We used simple, twosided, ttests (at 5% level) to measure if an observed change in a metric was statistically significant. We found that Block Execution time increases by around 10%. An increase of 6% can be attributed to node tracking and rent computations., An increase of 4% can be attributed to rent collection and t...
 RSK’s Pegout efficiency improvement — Segwit (part 3/3) RSK’s Pegout efficiency improvement — Segwit (part 3/3)  Coauthors: Ramsès FernàndezValència and Nicolás Vescovo In the previous posts (part 1 & part 2) on our RSK’s Pegout efficiency improvement series, I described the main actors of the architecture, the limitations of the current design and a brief introduction to Segwit versions. I also went more indepth and described the different proposals studied with their corresponding implementation: Segwit v0 and Segwit v1/Taproot (FROST, ICEFROST and MuSig2). In this last post, I will end up with a comparison of the proposals and a conclusion. — Comparison  — Summary. — — PowHSM Complexity  When we refer to the complexity we are talking about code and data structure. — Segwit v0. — The complexity here involves only code, as the only change involves the fields that now compose the digest of the signature, and where (in the transaction) to put the resulting signature. We consider that comparing the rest of the protocols is a loweffort change, not to underestimate the complexity but to expose it compared to the rest. — Segwit v1. — In both cases, it is easy to see that FROST would be much more complex than MuSig2. In the case of Musig2, it is a “medium” complexity. We can highlight: that the Bitcoin transaction parsing by the powHSM changes (we have to account for witness parsing, which is currently no...
 RSK’s Pegout efficiency improvement — Segwit (part 2/3) RSK’s Pegout efficiency improvement — Segwit (part 2/3)  Coauthors: Ramsès FernàndezValència and Nicolás Vescovo In the previous post on our RSK’s Pegout efficiency improvement series, I described the main actors of the architecture, the limitations of the current design and a brief introduction to Segwit versions. In this post, I go even more indepth and describe the proposals studied: Segwit v0 and Segwit v1/Taproot (FROST, ICEFROST and MuSig2). And I also explain the modifications required to implement each scheme. In the next and last post, I will wrap up with the most interesting part: a comparison of the proposals and a conclusion. — Studied Proposals  — Segwit v0. — This proposal only requires minor changes to the current RSK Peg implementation. That is why we will not do any specific analysis in this section as it won’t require any significant change in the signature scheme. It is still a regular “m of n” multisignature scheme in which a group of m participants signs the transaction, and the data published onchain is n public keys and m signatures. The implementation of this proposal involves moving the redeem script and the signatures to the witness data section, which drastically reduces the fee cost of the transaction. The main advantages and drawbacks of this mechanism will be discussed in detail when compared with the rest of the proposals. — MuSig2 (Segwit...




