Blockchains implement decentralized monetary systems and applications. Recent advancements enable what we call tethering a blockchain to a primary blockchain, securing the tethered chain by nodes that post primary-chain tokens as collateral. The collateral ensures nodes behave as intended, until they withdraw it. Unlike a Proof of Stake blockchain which uses its own token as collateral, using primary-chain tokens shields the tethered chain from the volatility of its own token. State-of-the-art tethered blockchains either rely on centralization, or make extreme assumptions: that all communication is synchronous, that operators remain correct even post-withdrawal, or that withdrawals can be indefinitely delayed by tethered-chain failures. We prove that with partial synchrony, there is no solution to the problem. However, under the standard assumptions that communication with the primary chain is synchronous and communication among the tethered chain nodes is partially synchronous, there is a solution. We present a tethered-chain protocol called Aegis. Aegis uses references from its blocks to primary blocks to define committees, checkpoints on the primary chain to perpetuate decisions, and resets to establish new committees when previous ones become obsolete. It ensures safety at all times and rapid progress when latency among Aegis nodes is low.
Securing data generated from diverse sensors poses a critical challenge in contemporary applications, particularly due to the escalating volume of data and its vulnerability to security breaches. Blockchain technology has emerged as a promising solution, yet the implementation of blockchain applications entails significant costs. Assessing the feasibility of such implementations through simulation is imperative but has been hindered by a lack of details survey about the simulation tools leading to wrong choice of tools by the researchers, as every application is unique and requires specific blockchain platform as per use. This paper covers an extensive survey of 32 simulation tools used in blockchain, focusing on their advantages, limitations, platform used, and capability to evaluate performance metrics of blockchain applications. This enables the researchers to choose the correct simulation tool for their work. The work allows the programmer to test their application using the correct simulation tool, saving implementation costs and time. Furthermore, we discussed CBlockSim, a simulation tool designed specifically for blockchain applications, elucidating its functionality through a comparative study of Bitcoin and Ethereum. Our work contributes to advancing the understanding and application of simulation tools in blockchain-based sensor data security, offering insights that can significantly enhance the security posture of sensor networks.
IoT systems are widely used in various applications, including healthcare, agriculture, manufacturing, and smart cities. However, these systems still have limitations, such as lack of security, high latency, energy inefficiency, the inefficiency of bandwidth utilization, and shortage of automaticity. The integration of edge computing and blockchain into IoT has been proposed to address these limitations. Yet, this integration is challenging and has not been deeply investigated. This paper aims to conduct a review of the integration of edge computing and blockchain into IoT systems. To the best of our knowledge, this is the first review paper that covers all aspects of system architectures and categories of blockchain-based edge deployment, complete security requirements, including confidentiality, integrity, authentication, authorization/access control, privacy, trust/confidence, transparency, availability, secure automaticity, and tolerance, and applications of blockchain-based edge potential usages with consideration of security requirements. Additionally, this review provides comprehensive discussions of challenges and insights into the future direction of blockchain-based edge IoT systems. The review aims to serve as an entry point for non-expert readers and researchers to various aspects of blockchain-based edge IoT systems.
Burhan Ul Islam Khan, Khang Wen Goh, Mohammad Shuaib Mir, Nur Fatin Liyana Mohd Rosely · 6 authors
As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.
The office attendance system has shifted from using physical forms to digital inputs to minimize data errors and data loss when taking attendance. Unfortunately, digital systems generally still use traditional databases where the admin's role is crucial, and there is potential for fraud (e.g., admitting attendance of a non-attending person or manipulating a targeted person’s log due to personal grudges, competition, or other reasons) if the admin is dishonest. In this paper, we propose Absenin, a blockchain-based office attendance system, which replaces the role of traditional databases with blockchain and smart contracts to make it secure from malicious admins and fair for other participants. We create an Attendance Smart Contract that will run on the Ethereum blockchain. Admins and employees will interact with this smart contract to carry out attendance system operations. Absenin is also designed to have real-time attendance data, but the attendance machine does not need to be connected to the Internet, which is a unique feature of our system that no previous works have attempted. Despite using blockchain and smart contracts, our evaluation results show that Absenin is able to produce relatively small processing delays, and gas usage on the blockchain is still far below the gas limit of the Ethereum mainnet. Therefore, we can assure that the system is feasible and can be applied to organizations with a scale of thousands, tens, or hundreds of thousands of employees.
We study long-term equilibria that arise in the token monetary policy, or tokenomics, design of proof-of-stake (PoS) blockchain systems that engage utility maximizing users and validators. Validators are system maintainers who get rewarded with tokens for performing the work necessary for the system to function properly, while users compete and pay with such tokens for getting a desired portion of the system service. <br/><br/>We study how the system service provision and suitable rewards schemes together can lead to equilibria with the following desirable characteristics (1) viability: the system keeps parties engaged, (2) decentralization and skin-in-the-game: multiple sufficiently invested validators are participating, (3) stability: the price path of the underlying token used to transact with the system does not change widely over time, and (4) feasibility: the mechanism is easy to implement as a smart contract, e.g., it does not require a fiat reserve on-chain to perform token buybacks or to perform bookkeeping of exponentially growing token holdings.<br/><br/>Our analysis enables us to put forward a novel generic mechanism for blockchain monetary policy that we call quantitative rewarding (QR). We investigate how to implement QR in single-token and two-token proof of stake (PoS) blockchain systems. The latter are systems that utilize one token for the users to pay the transaction fees and a different token for the validators to participate in the PoS protocol and get rewarded. Our approach demonstrates a concrete advantage of the two-token setting in terms of the ability of the QR mechanism to be realized effectively and provide good equilibria. Our analysis also reveals an inherent limitation of the single token setting in terms of implementing an effective blockchain monetary policy - a distinction that is, to the best of our knowledge, highlighted for the first time.licy - a distinction that is, to the best of our knowledge, highlighted for the first time.
Supported by cloud computing,FederatedLearning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security and fairness of FL implementation, numerousBlockchain-empoweredFederatedLearning (BFL) frameworks have emerged accordingly. Among them, consensus algorithms play a pivotal role in determining the scalability, security, and consistency of BFL systems. Existing consensus solutions to block producer selection and reward allocation either focus on well-resourced scenarios or accommodate BFL based on clients' contributions to model training. However, these approaches limit consensus efficiency and undermine reward fairness, due to involving intricate consensus processes, disregarding clients' contributions during blockchain consensus, and failing to address lazy client problems (malicious clients plagiarizing local model updates from others to reap rewards). Given the aforementioned challenges, we make the first attempt to design a joint solution for efficient consensus and fair reward allocation in heterogeneous BFL systems with lazy clients. Specifically, we introduce a generalizable BFL workflow that can address lazy client problems well. Based on it, the global contribution of BFL clients is decoupled into five dominant metrics, and the block producer selection problem is formulated as a reward-constraint contribution maximization problem. By addressing this problem, the optimal block producer that maximizes global contribution can be identified to orchestrate consensus processes, and rewards are distributed to clients in proportion to their respective global contributions. To achieve it, we develop aContext-awareProof-of-Contribution consensus algorithm named CPoC to reach consensus and incentive simultaneously, followed by theoretical analysis of lazy client problems and privacy issues. Empirical results on widely-used datasets demonstrate the effectiveness of our design in improving consensus efficiency and maximizing global contribution.
Federated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data. Nevertheless, in practice, the data distributions among these organizations are often non-independent and identically distributed (non-IID), which poses significant challenges for traditional federated learning. To tackle this challenge, we present a hierarchical federated learning framework based on blockchain technology, which is designed to enhance the training of non-IID data., protect data privacy and security, and improve federated learning performance. The framework builds a global shared pool by constructing a blockchain system to reduce the non-IID degree of local data and improve model accuracy. In addition, we use smart contracts to distribute and collect models and design a main blockchain to store local models for federated aggregation, achieving decentralized federated learning. We train the MLP model on the MNIST dataset and the CNN model on the Fashion-MNIST and CIFAR-10 datasets to verify its feasibility and effectiveness. The experimental results show that the proposed strategy significantly improves the accuracy of decentralized federated learning on three tasks with non-IID data.
In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamically adjusted truth discovery method safeguarding the truth of high-value price oracle tasks. In the truth aggregation stage, we enhance future considerations to improve the precision of aggregated truth. During the credibility update phase, credibility is dynamically assessed based on the task's value and the Cumulative Potential Economic Contribution (CPEC) of information sources. Experimental results demonstrate a significant reduction in data deviation by 65.8\% and potential economic loss by 66.5\%, compared to the baseline scheme, in the presence of high-value attacks.
Rafael Belchior, Limaris Torres, Jonas Pfannschmidt, André Vasconcelos · 5 authors
With the evolution of distributed ledger technology (DLT), several blockchains that provide enhanced privacy guarantees and features, including Corda, Hyperledger Fabric, and Canton, are being increasingly adopted. These distributed ledgers only provide partial consistency, meaning that participants can observe the same ledger differently, i.e., observe some transactions but not others, providing higher levels of privacy to the end-user. Choosing privacy instead of transparency leads to delicate trade-offs that are difficult to manage during runtime, hampering the development of applications that depend on reasoning about shared state, e.g., asset transfers across blockchains. We propose using the concept of blockchain view (view) – an abstraction of the state a participant can access at a certain point to address this problem. Views allow us to systematically reason about either state partitions within the same DLT or an integrated view spanning across several DLTs. We introduce BUNGEE (Blockchain UNifier view GEnErator), the first DLT view generator, to allow capturing snapshots, constructing views from these snapshots, and merging views according to a set of rules specified by the view stakeholders. Creating views and operating views allows new applications built on top of dependable blockchain interoperability, such as stakeholder-centric snapshots for audits, cross-chain analysis, blockchain migration, and combined on-chain-off-chain analytics.
Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in diverse domains, security, and privacy concerns arise due to the distributed nature of this solution. Therefore, integrating this strategy with Blockchain technology has been consolidated as a preferred choice to ensure the privacy and security of participants. This paper explores the research efforts carried out by the scientific community to define privacy solutions in scenarios adopting Blockchain-Enabled FL. It comprehensively summarizes the background related to FL and Blockchain, evaluates existing architectures for their integration, and the primary attacks and possible countermeasures to guarantee privacy in this setting. Finally, it reviews the main application scenarios where Blockchain-Enabled FL approaches have been proficiently applied. This survey can help academia and industry practitioners understand which theories and techniques exist to improve the performance of FL through Blockchain to preserve privacy and which are the main challenges and future directions in this novel and still under-explored context. We believe this work provides a novel contribution respect to the previous surveys and is a valuable tool to explore the current landscape, understand perspectives, and pave the way for advancements or improvements in this amalgamation of Blockchain and Federated Learning.
The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the ``6A-goal" of ACS and propose potential avenues for future research.
Mobile Crowdsensing (MCS) is a paradigm where a crowdsourcer recruits a set of workers through a campaign to collect data using sensors in their mobile device. This process greatly reduces the costs of data collection processes; however, most of the historically proposed systems are centralized. Since this makes the MCS platform a single point of failure, there is an increasing interest in decentralized blockchain-based solutions; regardless, most of the current proposals have a vertical focus and do not account for the heterogeneity of MCS. We propose a decentralized high-level architecture for MCS, based on Distributed Ledger Technology (DLT), that is adaptable to most MCS deployments. We then implement our architecture using the IOTA protocols and evaluate its performance over a real deployment in terms of scalability, showing its advantages over classic blockchains for MCS data.
Abstract The built environment fundamentally suffers from organisational fragmentation in various aspects, such as data flow, finance, and supply chains. Blockchain technology can be considered a transformative solution to the inherent fragmentation of this industry. This chapter first defines the basics of blockchain technology to show how a peer-to-peer network could enable a decentralised, traceable, and immutable information system across the life cycles of built assets. Then, an overview of blockchain literature within the context of a circular economy, with real-life examples and the current state of blockchain adoption in the circular built environment, is presented, and the role that this technology plays in addressing certain circular strategies is discussed. Afterward, implementation challenges and incentives are identified to set realistic expectations regarding the capabilities of blockchain technologies. Emerging concepts within blockchain technologies are then presented to give insights into prospects beyond current literature and use cases in the circular built environment. Finally, the future of blockchain technology in a circular built environment is discussed to present the applicability of blockchain and its possible integration with other emerging digitalisation tools, such as building information modelling (BIM) and material passports, in wider domains of circular, smart cities and communities.
Trustless tracking of Resident Space Objects (RSOs) is crucial for Space Situational Awareness (SSA), especially during adverse situations. The importance of transparent SSA cannot be overstated, as it is vital for ensuring space safety and security. In an era where RSO location information can be easily manipulated, the risk of RSOs being used as weapons is a growing concern. The Tracking Data Message (TDM) is a standardized format for broadcasting RSO observations. However, the varying quality of observations from diverse sensors poses challenges to SSA reliability. While many countries operate space assets, relatively few have SSA capabilities, making it crucial to ensure the accuracy and reliability of the data. Current practices assume complete trust in the transmitting party, leaving SSA capabilities vulnerable to adversarial actions such as spoofing TDMs. This work introduces a trustless mechanism for TDM validation and verification using deep learning over blockchain. By leveraging the trustless nature of blockchain, our approach eliminates the need for a central authority, establishing consensus-based truth. We propose a state-of-the-art, transformer-based orbit propagator that outperforms traditional methods like SGP4, enabling cross-validation of multiple observations for a single RSO. This deep learning-based transformer model can be distributed over a blockchain, allowing interested parties to host a node that contains a part of the distributed deep learning model. Our system comprises decentralised observers and validators within a Proof of Stake (PoS) blockchain. Observers contribute TDM data along with a stake to ensure honesty, while validators run the propagation and validation algorithms. The system rewards observers for contributing verified TDMs and penalizes those submitting unverifiable data.
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead.
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Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Mobile crowdsourcing (MCS) is an emerging paradigm that enables the outsourcing of a complex task to a group of mobile devices. The ability to utilize the collective power of mobile devices and human intelligence makes MCS a significant tool in various scenarios. Nevertheless, it faces practical challenge in protecting user privacy due to the sensitive nature of information collected by mobile devices. Additionally, the inherent openness of MSC and the heterogeneity of mobile devices raise reliability concerns among participants. To address these challenges, by integrating Federated Learning with the pairwise additive masking technique and the Chinese Remainder Theorem, we propose a Blockchain-based Privacy-preserving Federated Learning (BPFL) framework for mobile crowdsourcing, which allows mobile participants to collaboratively solve a crowdsourced machine learning task while preserving privacy. Besides, it employs blockchain technology to record the training process in a transparent and tamper-proof ledger. This ledger guarantees the verifiability of aggregation results and the fair distribution of training rewards, thereby enhancing trust and fairness. We prove that our BPFL supports privacy protection and trust mechanism simultaneously and resists inference and collusion attacks. Experimental results show that our BPFL can achieve high performance in terms of computation cost, communication cost and model accuracy, which is friendly for mobile users with resource-constrained devices in MCS ecosystems.
Xiaohu Du, Xiao Chen, Jialun Cao, Ming Wen · 6 authors
Federated learning (FL) is an emerging machine learning paradigm that aims to address the problem of isolated data islands. To preserve privacy, FL allows machine learning models and deep neural networks to be trained from decentralized data kept privately at individual devices. FL has been increasingly adopted in missioncritical fields such as finance and healthcare. However, bugs in FL systems are inevitable and may result in catastrophic consequences such as financial loss, inappropriate medical decision, and violation of data privacy ordinance. While many recent studies were conducted to understand the bugs in machine learning systems, there is no existing study to characterize the bugs arising from the unique nature of FL systems. To fill the gap, we collected 395 real bugs from six popular FL frameworks (Tensorflow Federated, PySyft, FATE, Flower, PaddleFL, and Fedlearner) in GitHub and StackOverflow, and then manually analyzed their symptoms and impacts, prone stages, root causes, and fix strategies. Furthermore, we report a series of findings and actionable implications that can potentially facilitate the detection of FL bugs.
This paper centers around a simple yet crucial question for everyday users: How should one choose their delegated validators within proof-of-stake (PoS) protocols, particularly in the context of Ethereum 2.0? This has been a long-overlooked gap, as existing studies have primarily focused on inter-committee (validator set) behaviors and activities, while neglecting the dynamic formation of committees, especially for individual stakeholders seeking reliable validators. Our study bridges this gap by diving into the delegation process (normal users delegate their small-value tokens to delegatees who later act as validators) before entering an actual consensus phase. We propose a Bayesian model to quantify normal users' trust in delegatees, which we further incorporate into a game-theoretical model to simulate users' reactions against a set of critical factors identified through extensive research (including 10+ staking service provider as well as 30+ PoS blockchains). Our results reveal that users tend to choose their delegatees and utilize their tokens by carefully weighing the delegation cost, the behaviors of other users, and the reputation of delegatees, ultimately reaching a Nash equilibrium. Unfortunately, the collective trend significantly increases the likelihood of token concentration on a small number of delegatees.
Bogdan-Ionut Pahontu, Adrian Petcu, Alexandru Predescu, Diana Arsene · 5 authors
Water is one of the most important resources in our lives, and because of this, the interest in water management systems is growing constantly. A primary concern regarding urban water distribution is how to build robust solutions to facilitate water monitoring flows with the support of consumer involvement. Crowdsensing solutions contribute to the involvement in social platforms for increased awareness about the importance of water resources based on incentives and rewards. Blockchain is one of the technologies that has become increasingly popular in the last few years. The possibility of using this architecture in such different sectors while integrating emerging concepts, such as crowdsensing, the Internet of Things, serious gaming, and decision support systems, offers a lot of alternatives and approaches for designing modern applications. This paper aims to present how these technologies can be combined in order to migrate the functionalities of a water distribution management system from a centralized architecture to a decentralized one by leveraging blockchain technologies. The proposed application was designed to facilitate incident reporting flows in public water distribution networks. The proposed solution was to migrate the rewarding mechanisms using the Ethereum infrastructure. The novelty of this solution is determined by the introduction of this decentralized approach into the architecture and also by increasing customer interest by offering tradeable rewards and dynamic subscription discounts. This results in a new decentralized architecture that allows for more transparent interactions between the water provider and clients and increases customer engagement to contribute to water reporting flows.
Francisco Moya, Francisco J. Quesada, Luis Martı́nez, Francisco J. Estrella
Abstract Nowadays, Internet of Things (IoT) devices, especially wearable devices, are commonly integrated into modern intelligent healthcare software. These devices enable medical practitioners to monitor pervasively patients’ parameters outside the clinical environment. However, the ease of manipulating wearable devices and their data streams raises concerns regarding patient privacy and data trust. Distributed ledger technologies (DLT) offer solutions to enhance resistance against information manipulation and eliminate single points of failure. By leaveraging DLT, wearable-based solutions can be developed with a wider range of capabilities. This paper carries out an analysis of shortcomings, limitations, potential applications and needs in the medical domain, to introduce Phonendo 1.0, a DLT–IoT-based platform designed to capture data streams from wearable devices and publishing them on a distributed ledger technology infrastructure. The architecture and its difference services are justified based on the identified needs and challenges in the medical domain.
Crowdsourcing has emerged as a prevalent method for mitigating the risks of correctness and security in outsourced cloud computing. This process involves an aggregator distributing tasks, collecting responses, and aggregating outcomes from multiple data sources. Such an approach harnesses the wisdom of crowds to accomplish complex tasks, enhancing the accuracy of task completion while diminishing the risks associated with the malicious actions of any single entity. However, a critical question arises: How can we ensure that the aggregator performs its role honestly and each contributor's input is fairly evaluated? In response to this challenge, we introduce a novel protocol termed $\mathsf{zkTI}. This scheme guarantees both the honest execution of the aggregation process by the aggregator and the fair evaluation of each data source. It innovatively integrates a cryptographic construct known as zero-knowledge proof with a category of truth inference algorithms for the first time. Under this protocol, the aggregation operates with both correctness and verifiability, while ensuring fair assessment of data source reliability. Experimental results demonstrate the protocol's efficiency and robustness, making it a viable and effective solution in crowdsourcing and cloud computing.
Sharded blockchain offers scalability, decentralization, immutability, and linear improvement, making it a promising solution for addressing the trust problem in large-scale collaborative IoT. However, a high proportion of cross-shard transactions can severely limit the performance of decentralized blockchain. Furthermore, the dynamic assemblage characteristic of collaborative sensing in sharded blockchain is often ignored. To overcome these limitations, we propose HMMDShard, a dynamic blockchain sharding scheme based on the Hidden Markov Model. HMMDShard leverages fine-grained blockchain sharding and fully embraces the dynamic assemblage characteristic of IoT collaborative sensing. By integrating the Hidden Markov Model, we achieve adaptive dynamic incremental updating of blockchain shards, effectively reducing cross-shard transactions across all shards. We conduct a comprehensive analysis of the security issues and properties of HMMDShard, and evaluate its performance through the implementation of a system prototype. The results demonstrate that HMMDShard significantly reduces the proportion of cross-shard transactions and outperforms other baselines in terms of system throughput and transaction confirmation latency.