In database applications involving sensitive data, the dual imperatives of data confidentiality and provable (verifiable) query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional databases, PoneglyphDB enhances confidentiality by ensuring that raw data remains exclusively with the host, while also enabling verifying the correctness of query responses by providing proofs to clients. The main innovation in this paper is proposing efficient ZKP designs (called circuits) for basic operations in SQL query processing. These basic operation circuits are then combined to form ZKP circuits for larger, more complex queries. PoneglyphDB's circuits are carefully designed to be efficient by utilizing advances in cryptography such as PLONKish-based circuits, recursive proof composition techniques, and designing with low-order polynomial constraints. We demonstrate the performance of PoneglyphDB with the standard TPC-H benchmark. Our experimental results show that PoneglyphDB can efficiently achieve both confidentiality and provability, outperforming existing state-of-the-art ZKP methods.
Cardano is a Proof-of-Stake cryptocurrency with a market capitalisation in the tens of billions of USD and a daily volume of hundreds of millions of USD. In this paper we reflect on applying formal methods, functional architecture and Haskell to building Cardano. We describe our strategy, projects, lessons learned, the challenges we face, and how we propose to meet them.
The Ethereum Global Network (EGN) hosts a complete ecosystem of decentralized services, including blockchains such as Ethereum mainnet but also exchange markets, content delivery networks, and many more. Service discovery is a fundamental mechanism in the EGN, allowing new nodes to look up and connect to other nodes already participating in one of these services. The current service discovery of the EGN, DISCv5, is not scalable and efficient enough to support the current and future needs of the ecosystem. We present DISC-NG, a novel service discovery protocol for the EGN that is scalable, efficient, and secure. DISC-NG leverages the EGN-wide DHT to allow service participation advertisements to meet service discovery requests. DISC-NG compensates the unbalance in service popularity and minimizes the potential for abuse by malicious nodes. We implement DISC-NG in devp2p, the network stack used by the majority of clients connecting to the EGN, as well as in a large-scale simulator. DISC-NG can discover services in the EGN faster than DISCv5 while being more robust to malicious nodes. DISC-NG is now in a staging phase and scheduled for deployment as an improvement to DISCv5.
Elvira Albert, MarÃa GarcÃa de la Banda, Alejandro Hernández-Cerezo, Alexey Ignatiev · 6 authors
Given a loop-free sequence of instructions, superoptimization techniques use a constraint solver to search for an equivalent sequence that is optimal for a desired objective. The complexity of the search grows exponentially with the length of the solution being constructed and the problem becomes intractable for large sequences of instructions. This paper presents a new approach to superoptimizing stack-bytecode via three novel components: (1) a greedy algorithm to refine the bound on the length of the optimal solution; (2) a new representation of the optimization problem as a set of weighted soft clauses in MaxSAT; (3) a series of domain-specific dominance and redundant constraints to reduce the search space for optimal solutions. We have developed a tool, named S uper S tack , which can be used to find optimal code translations of modern stack-based bytecode, namely WebAssembly or Ethereum bytecode. Experimental evaluation on more than 500,000 sequences shows the proposed greedy, constraint-based and SAT combination is able to greatly increase optimization gains achieved by existing superoptimizers and reduce to at least a fourth the optimization time.
Amir M. Ebrahimi, Bram Adams, Gustavo A. Oliva, Ahmed E. Hassan
The proxy pattern is a well-known design pattern with numerous use cases in several sectors of the software industry. As such, the use of the proxy pattern is also a common approach in the development of complex decentralized applications (DApps) on the Ethereum blockchain. Despite the importance of proxy contracts, little is known about (i) how their prevalence changed over time, (ii) the ways in which developers integrate proxies in the design of DApps, and (iii) what proxy types are being most commonly leveraged by developers. This study bridges these gaps through a comprehensive analysis of Ethereum smart contracts, utilizing a dataset of 50 million contracts and 1.6 billion transactions as of September 2022. Our findings reveal that 14.2% of all deployed smart contracts are proxy contracts. We show that proxy contracts are being more actively used than non-proxy contracts. Also, the usage of proxy contracts in various contexts, transactions involving proxy contracts, and adoption of proxy contracts by users have shown an upward trend over time, peaking at the end of our study period. They are either deployed through off-chain scripts or on-chain factory contracts, with the former and latter being employed in 39.1% and 60.9% of identified usage contexts in turn. We found that while the majority (67.8%) of proxies act as an interceptor, 32.2% enables upgradeability. Proxy contracts are typically (79%) implemented based on known reference implementations with 29.4% being of type ERC-1167, a class of proxies that aims to cheaply reuse and clone contracts' functionality. Our evaluation shows that our proposed behavioral proxy detection method has a precision and recall of 100% in detecting active proxies. Finally, we derive a set of practical recommendations for developers and introduce open research questions to guide future research on the topic.
Alexandr Kuznetsov, Alex Rusnak, Anton Yezhov, Dzianis Kanonik · 6 authors
Zero-knowledge proofs have emerged as a powerful tool for enhancing privacy and security in blockchain applications. However, the efficiency and scalability of proof systems remain a significant challenge, particularly in the context of Merkle tree inclusion proofs. Traditional proof aggregation techniques based on AND logic suffer from a high verification complexity and data communication overhead, limiting their practicality for large-scale applications. In this paper, we propose a novel proof aggregation approach based on OR logic, which enables the generation of compact and universally verifiable proofs for Merkle tree inclusion. By adapting and extending the concept of OR composition from Sigma protocols, we achieve a proof size that is independent of the number of leaves in the tree, and verification can be performed using any single valid leaf hash. This represents a significant improvement over AND aggregation, which requires the verifier to process all leaf hashes. We formally define the OR aggregation logic; describe the process of generating universal proofs; and provide a comparative analysis that demonstrates the advantages of our approach in terms of proof size, verification data, and universality. Furthermore, we discuss the potential of combining OR and AND aggregation logics to create complex acceptance functions, enabling the development of expressive and efficient proof systems for various blockchain applications. The proposed techniques have the potential to significantly enhance the scalability, efficiency, and flexibility of zero-knowledge proof systems, paving the way for more practical and adaptive solutions in large-scale blockchain ecosystems.
Ethereum 2.0 is the second-largest cryptocurrency by market capitalization and a widely used smart contract platform. Therefore, examining the reliability of Ethereum 2.0's incentive mechanism is crucial, particularly its effectiveness in encouraging validators to adhere to the Ethereum 2.0's protocol. This paper studies the incentive mechanism of Ethereum 2.0 and evaluates its robustness by analyzing the interaction between block proposers and attesters in a single slot. To this end, we use Bayesian games to model the strategies of block proposers and attesters and calculate their expected utilities. Our results demonstrate that the Ethereum 2.0 incentive mechanism is incentive-compatible and promotes cooperation among validators. We prove that a Bayesian Nash equilibrium and an ex ante dominant strategy exist between the block proposer and attesters in a single slot. Our research provides a solid foundation for further analysis of Ethereum 2.0's incentive mechanism and insights for individuals considering participation as a validator in Ethereum 2.0.
Multiple sequence alignment (MSA) is a fundamental algorithm in bioinformatics. In a situation when the alignment might need to be protected while revealing the other information such the input sequences and the alignment score, zero knowledge proof can be used. In this paper, a validator checks the consistency between the input sequence and the alignment, and between the alignment and the alignment score. The validator is written in Circom language which will be compile into a circuit. Using a zero knowledge prove system called zkSNARK, a cryptographic proof is generates for the circuit and its input. This proof demonstrates that all inputs are consistent without revealing the actual alignment.
This article examines the significant challenges encountered in implementing sharding within distributed replication systems. It identifies the impediments of achieving consensus among large participant sets, leading to scalability, throughput, and performance limitations. These issues primarily arise due to the message complexity inherent in consensus mechanisms. In response, we investigate the potential of sharding to mitigate these challenges, analyzing current implementations within distributed replication systems. Additionally, we offer a comprehensive review of replication systems, encompassing both classical distributed databases as well as Distributed Ledger Technologies (DLTs) employing sharding techniques. Through this analysis, the article aims to provide insights into addressing the scalability and performance concerns in distributed replication systems.
The widespread availability of tools to collect and share spatial data enables us to produce a large amount of geographic information on a daily basis. This enormous production of spatial data requires scalable data management systems. Geospatial architectures have changed from clusters to cloud architectures and more parallel and distributed processing platforms to be able to tackle these challenges. Peer-to-peer (P2P) systems as a backbone of distributed systems have been established in several application areas such as web3, blockchains, and crypto-currencies. Unlike centralized systems, data storage in P2P networks is distributed across network nodes, providing scalability and no single point of failure. However, managing and processing queries on these networks has always been challenging. In this work, we propose a spatio-temporal indexing data structure, DSTree. DSTree does not require additional Distributed Hash Trees (DHTs) to perform multi-dimensional range queries. Inserting a piece of new geographic information updates only a portion of the tree structure and does not impact the entire graph of the data. For example, for time-series data, such as storing sensor data, the DSTree performs around 40% faster in spatio-temporal queries for small and medium datasets. Despite the advantages of our proposed framework, challenges such as 20% slower insertion speed or semantic query capabilities remain. We conclude that more significant research effort from GIScience and related fields in developing decentralized applications is needed. The need for the standardization of different geographic information when sharing data on the IPFS network is one of the requirements.
Principal Engineer, Discover Financial Services, Houston, TX, USA, AdisheshuReddy Kommera
The Event-Driven Data Mesh Integration pattern revolutionizes modern data sharing by blending eventdriven architecture and the data mesh paradigm. This innovative approach decentralizes data ownership, enabling organizational domains to manage their data autonomously while ensuring real-time responsiveness and seamless scalability. Central to the pattern are event broker layers, domain-oriented data producers, event enrichment nodes, and a self-serve data product catalog. Key features include schema validation, policy-based governance, and real-time enrichment, fostering efficiency, compliance, and agility. Integrating AI-powered self-healing mechanisms further enhances resilience, automates recovery processes, and optimizes resource allocation. Applications span various sectors, from operational systems to analytics pipelines, enabling real-time decision-making and continuous improvement. This approach empowers organizations to innovate faster while maintaining robust data governance, scalability, and interoperability across autonomous domains, paving the way for intelligent and dynamic data ecosystems.
This PhD project proposes the theoretical and technological foundations of an approach for decentralized processing of streaming knowledge graphs, where autonomous reasoners may combine individual and collective processing of continuous data. These decentralized stream processors shall be capable of sharing not only data stream knowledge, but also processing duties, using collaboration and negotiation protocols. Moreover, commonly agreed semantic vocabularies will be used to address the high dynamicity of reasoners' knowledge and goals. The approach proposed in this project goes beyond previous works on stream reasoning, enabling the self-organization and coordination among distributed stream reasoners, based on techniques and principles inspired by Multi-Agent systems. On the one hand, it adds the ability to explicate processing goals, capabilities and knowledge, while on the other it exploits potential ways of interconnecting them in ways that expand their combined capacity/efficacy for managing highly dynamic flows of streaming knowledge. Through this approach, efficient local stream processors can establish cooperative processing schemes, respecting data privacy restrictions and data locality requirements through the exchange of streaming Knowledge Graphs.
Because of its potential to upend established industries and alter how apps are developed, run, used, and promoted in the near future, blockchain technology has recently gained growing interest on a global scale.Although this technology was initially intended to be an immutable and distributed ledger for avoiding cryptocurrency double spending, it is currently anticipated to serve as the main support system for businesses by facilitating interoperability and collaboration across firms.In this setting, consortium blockchains have come to light as an intriguing architectural idea that makes use of the decentralized governance of public blockchains while gaining the efficiency and anonymity of private blockchains for transactions.Despite the fact that blockchain technology has been the subject of several research, the idea of consortium blockchains has received very little attention in the literature.This article offers a thorough examination of consortium blockchains' topologies, technological underpinnings, and applications in order to close this gap.
A novel data structure that enables the storage and retrieval of linear array numeric data with logarithmic time complexity updates, range sums, and rescaling is introduced and studied. Computing sums of ranges of arrays of numbers is a common computational problem encountered in data compression, coding, machine learning, computational vision, and finance, among other fields. Efficient data structures enabling log n updates of the underlying data (including range updates), queries of sums over ranges, and searches for ranges with a given sum have been extensively studied (n being the length of the array). Two solutions to this problem are well-known: Fenwick trees (also known as Binary Indexed Trees) and Segment Trees. The new data structure extends the capabilities for the first time to further enable multiplying (rescaling) ranges of the underlying data by a scalar as well in log n. Scaling by 0 can be enabled, with the effect that subsequent updates may take (log n)2time. The new data structure introduced here consists of a pair of interacting Fenwick tree-like structures, one of which holds the unscaled values and the other of which holds the scalars. Experimental results demonstrating performance improvements for the multiplication operation on arrays from a few dozen to over 30 million data points are discussed. This research was done as part of Ajna Labs in the course of developing a decentralized finance protocol. It enables an efficient on-chain encoding and processing of an order book-like data structure used to manage lending, interest, and collateral.
Abstract An emerging approach that addresses data heterogeneity challenges is the mediator-based architecture allowing transparent access to the data stored in many sources. Due to the growing diversity of data sources, the data integration process becomes a performance and administrative bottleneck. When dealing with decentralized heterogeneous data sources, the mediator-based technique is typically used to integrate the data. It describes a collection of applications that follow a number of data sources. The importance of analyzing and synthesizing the collected data has lately increased for academics researching autonomous and heterogeneous software systems. This study uses a mediator-based information integration model to improve pattern-based reasoning and overcome grammatical problems with integrating diverse information from web sources (IATs). The main goal of this study is to improve the mediator design for usage in the mediator-based information integration paradigm in order to address the problems caused by syntactic heterogeneity. Using our recommended methodology and enhancement strategy, the proposed technique would choose the pertinent domain from a variety of vendor-related data sources and antiquated file systems and deliver the necessary information set from heterogeneous data sources. Our suggested extended design functions well in the online bookstore as well, where there are several data sources and antiquated file systems. Future applications of this research include providing a thorough syntactic method that must be used to integrate data from various sources within the same organization into any Executive Support System (ESS).
Nikos Kefalakis, Aikaterini Roukounaki, John Soldatos, Mauro Isaja
This chapter presents a dynamic and programmable distributed data analytics solution for industrial environments. The solution includes an edge analytics engine for analytics close to the field and in line with the edge computing paradigm. Each edge analytics engine instance is flexible and dynamically configurable based on an Analytics Manifest (AM). It is also based on distributed ledger technologies for configuring analytics tasks that span multiple edge nodes and instances of the edge analytics engine. In particular, it leverages ledger services for synchronizing and combining various AMs in factory wide analytics tasks. Based on these mechanisms, the presented distributed data analytics infrastructure is therefore flexible, configurable, dynamic and resilient. Moreover, it is open source and provides Open APIs (Application Programming Interfaces) that enable access to its functionalities. These features make it unique and valuable for vendors and integrators of industrial automation solutions.
The subject of the study. The effective functioning of decentralized autonomous organizations<br> (DAOs) is associated with moving from the digital level to the real level in order to find or provide<br> information and communicate with the real environment. Obtaining or transmitting necessary<br> information about certain real–world facts, or information from other digital databases that cannot<br> interact directly with the DAO, is provided through oracles as intermediaries between decentralized<br> databases and real–world events.<br> Results of work. In this article, we consider the oracle as a tool for collecting information for DAO,<br> able to ensure transition of information from the unstable and unsecured physical world in the digital<br> environment of blockchain technology, where information acquires new characteristics and values.<br> Oracle, as a tool with a function of information delivery, combines the functions of finding the necessary<br> information with the functions of ensuring the authenticity and encryption of data in the required format.<br> Conclusions. This article gives a general understanding of the concept of the oracle, and its<br> significance for the work of DAO, as well as development of digital projects built using blockchain<br> technology. Classification of oracles for work with DAO is also given together with characteristics of<br> their possible role for organization of data supply. In addition, the possibility of building a DAO with oracle<br> functions to perform non–standard tasks for the transportation and adaptation of information for<br> various purposes in a blockchain environment is considered. The article also presents main problems<br> that may arise in interactions of oracles with DAO, and suggests possible solutions.
Kevin Wittek, Dominik Krakau, Neslihan Wittek, James H. Lawton · 5 authors
Proof of Existence as a blockchain service has first been published in 2013 as a public notary service on the Bitcoin network and can be used to verify the existence of a particular file in a specific point of time without sharing the file or its content itself. This service is also available on the Ethereum based bloxberg network, a decentralized research infrastructure that is governed, operated and developed by an international consortium of research facilities. Since it is desirable to integrate the creation of this proof tightly into the research workflow, namely the acquisition and processing of research data, we show a simple to integrate MATLAB extension based solution with the concept being applicable to other programming languages and environments as well.
Recursive Length Prefix (RLP) is used to encode a wide variety of data in Ethereum, including transactions. The work described in this paper provides a formal specification of RLP encoding and a verified implementation of RLP decoding, developed in the ACL2 theorem prover. This work has led to improvements to the Ethereum documentation and additions to the Ethereum test suite.
There are thousands of projects worldwide based primarily on blockchain technology. These have a large number of users and hundreds of use cases. One of the most popular is the use of cryptocurrencies and their benefits against money without intrinsic value (fiat money) and centralized financial solutions. However, although thousands of new transactions are carried out daily in different platforms, uniform and standardized information does not exist to be able to manage the large amount of data that is generated and exchanged between users through transactions and the generation of new blocks. This research reports the development of BLONDiE, an ontology that allows the semantic representation of knowledge to describe the native structure and related information of the three most relevant blockchain projects to date: Bitcoin, Ethereum and in the recent 1.0 version extends its definitions to include Hyperledger, specifically the Hyperledger Fabric infrastructure. Its use allows having common data formats of different platforms for further processing, such as the execution of semantic queries.