zk-SNARKs are zero-knowledge succinct non-interactive arguments of knowledge that allow a prover to convince a verifier of a statement without revealing details. They work by converting a function and its inputs/outputs into a quadratic arithmetic program (QAP) represented as polynomials. This allows a verifier to efficiently check a proof generated by the prover using techniques like Lagrange interpolation and pairings on elliptic curves to ensure the polynomials satisfy the QAP without directly evaluating the function. The setup requires a "trusted setup" but then allows very efficient verification.
ZK Study Club: Supernova (Srinath Setty - MS Research)Alex Pruden
This week, Srinath Setty (MS Research) will present SuperNova, a new recursive proof system for incrementally producing succinct proofs of correct execution of programs on a stateful machine with a particular instruction set (e.g., EVM, RISC-V). A distinguishing aspect of SuperNova is that the cost of proving a step of a program is proportional only to the size of the circuit representing the instruction invoked by the program step. This is a stark departure from prior works that employ universal circuits where the cost of proving a program step is proportional at least to the sum of sizes of circuits representing each supported instruction—even though a particular program step invokes only one of the supported instructions. Naturally, SuperNova can support a rich instruction set without affecting the per-step proving costs. SuperNova achieves its cost profile by building on Nova, a prior high-speed recursive proof system, and leveraging its internal building block, folding schemes, in a new manner. We formalize SuperNova’s approach as a way to realize non-uniform IVC, a generalization of IVC. Furthermore, SuperNova’s prover costs and the recursion overhead are the same as Nova’s, and in fact, SuperNova is equivalent to Nova for machines that support a single instruction.
https://eprint.iacr.org/2022/1758
ZK Study Club: Supernova (Srinath Setty - MS Research)Alex Pruden
This week, Srinath Setty (MS Research) will present SuperNova, a new recursive proof system for incrementally producing succinct proofs of correct execution of programs on a stateful machine with a particular instruction set (e.g., EVM, RISC-V). A distinguishing aspect of SuperNova is that the cost of proving a step of a program is proportional only to the size of the circuit representing the instruction invoked by the program step. This is a stark departure from prior works that employ universal circuits where the cost of proving a program step is proportional at least to the sum of sizes of circuits representing each supported instruction—even though a particular program step invokes only one of the supported instructions. Naturally, SuperNova can support a rich instruction set without affecting the per-step proving costs. SuperNova achieves its cost profile by building on Nova, a prior high-speed recursive proof system, and leveraging its internal building block, folding schemes, in a new manner. We formalize SuperNova’s approach as a way to realize non-uniform IVC, a generalization of IVC. Furthermore, SuperNova’s prover costs and the recursion overhead are the same as Nova’s, and in fact, SuperNova is equivalent to Nova for machines that support a single instruction.
https://eprint.iacr.org/2022/1758
Paper: https://eprint.iacr.org/2022/1355
Plonk is a widely used succinct non-interactive proof system that uses univariate polynomial commitments. Plonk is quite flexible: it supports circuits with low-degree ``custom'' gates as well as circuits with lookup gates (a lookup gate ensures that its input is contained in a predefined table). For large circuits, the bottleneck in generating a Plonk proof is the need for computing a large FFT.
In this work, the authors present HyperPlonk, an adaptation of Plonk to the boolean hypercube, using multilinear polynomial commitments. HyperPlonk retains the flexibility of Plonk but provides several additional benefits. First, it avoids the need for an FFT during proof generation. Second, and more importantly, it supports custom gates of much higher degree than Plonk without harming the running time of the prover. Both of these can dramatically speed up the prover's running time. Since HyperPlonk relies on multilinear polynomial commitments, the authors revisit two elegant constructions: one from Orion and one from Virgo. The authors also show how to reduce the Orion opening proof size to less than 10kb (an almost factor 1000 improvement) and show how to make the Virgo FRI-based opening proof simpler and shorter.
A decade of active research has led to practical constructions of zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) that are now being used in a wide variety of applications. Despite this astonishing progress, overheads in proof generation time remain significant.
In this work, we envision a world where consumers with low computational resources can outsource the task of proof generation to a group of untrusted servers in a privacy-preserving manner. The main requirement is that these servers should be able to collectively generate proofs at a faster speed (than the consumer). Towards this goal, we introduce a framework called zk-SNARKs-as-a-service () for faster computation of zk-SNARKs. Our framework allows for distributing proof computation across multiple servers such that each server is expected to run for a shorter duration than a single prover. Moreover, the privacy of the prover's witness is ensured against any minority of colluding servers.
We design custom protocols in this framework that can be used to obtain faster runtimes for widely used zk-SNARKs, such as Groth16 [EUROCRYPT 2016], Marlin [EUROCRYPT 2020], and Plonk [EPRINT 2019]. We implement proof of concept zkSaaS for the Groth16 and Plonk provers. In comparison to generating these proofs on commodity hardware, we show that not only can we generate proofs for a larger number of constraints (without memory exhaustion), but can also get speed-up when run with 128 parties for constraints with Groth16 and gates with Plonk.
https://eprint.iacr.org/2023/905
The following slides explains about elliptic curves, their interpretation over Gallois finite fields, algorithms that reduces arithmetic computational requirements and primarly applications of the ECC.
Credit : Nusrat Jahan & Fahima Hossain , Dept. of CSE, JnU, Dhaka.
Randomized Algorithm- Advanced Algorithm, Deterministic, Non Deterministic, LAS Vegas, MONTE Carlo Algorithm.
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms
that use space that is linear or sublinear in the dimension. We prove general results showing that any sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques such as Frequent Directions and random projections to derive efficient and practical algorithms for these problems, which we validate over real-world data sets. Our main technical contribution is to prove matrix perturbation
inequalities for operators arising in the computation of these measures.
-Proceedings: https://arxiv.org/abs/1804.03065
-Origin: https://arxiv.org/abs/1804.03065
Approximating Value of pi(Π) using Monte Carlo Iterative MethodNischal Lal Shrestha
1
Introduction
Monte Carlo methods (or Monte Carlo experiments) are a broad class of computa-
tional algorithms that rely on repeated random sampling to obtain numerical results.
Their essential idea is using randomness to solve problems that might be deterministic
in principle. They are often used in physical and mathematical problems and are most
useful when it is difficult or impossible to use other approaches. Monte Carlo methods
are mainly used in three problem classes: optimization, numerical integration, and
generating draws from a probability distribution.
Monte Carlo methods vary, but tend to follow a particular pattern
• Define a domain of possible inputs
• Generate inputs randomly from a probability distribution over the domain
• Perform a deterministic computation on the inputs
• Aggregate the results
zkStudy Club: Subquadratic SNARGs in the Random Oracle ModelAlex Pruden
Slides for Eylon Yogev's (Bar-Ilan University) presentation at ZKStudyClub, covering his new work (co-authored w/ Alessandro Chiesa of UC Berkeley) about SNARGs in the random oracle model of sub- quadratic complexity.
Link to the original paper: https://eprint.iacr.org/2021/281.pdf
Brief introduction to Algorithm analysis Anantha Ramu
Slide explains concepts
1. What is Asymptotic analysis
2. Why do we need it
3. Examples of Notation
4. What are the various kinds of Asymptotic analysis
5. How to compute Big O Notation
6. Big Oh examples
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.