Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan · 5 authors
Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services. However, traditional surveys cannot reflect the actual conditions of customers' due to the limited questions. Besides, survey results are affected by various subjective factors. In contrast, the recorded usages of IoT devices reflect customers' behaviours more comprehensively and accurately. We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology. The FL consists of two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively. The mobile edge computing server's high computation power can assist customers' training locally. Customers first collect data from various IoT devices using phones, and then download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model, customers sign on their models respectively and send them to the blockchain. We use the blockchain to replace the centralized aggregator which belongs to the third party in FL. In the second stage, miners calculate the averaged model using the collected models sent from customers. By the end of the crowdsourcing job, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. Besides, to attract more customers to participate in the crowdsourcing FL, we design an incentive mechanism, which awards participants with coins that can be used to purchase other services provided by the company.
Advances in Blockchain and distributed ledger technologies are driving the rise of incentivized social media platforms over Blockchains, where no single entity can take control of the information and users can receive cryptocurrency as rewards for creating or curating high-quality contents. This paper presents an empirical analysis of Steemit, a key representative of the emerging incentivized social media platforms over Blockchains, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit, users create contents as posts which get curated based on votes from other users. The platform periodically issues cryptocurrency as rewards to creators and curators of popular posts. Although such a reward system is originally driven by the desire to incentivize users to contribute to high-quality contents, our analysis of the underlying cryptocurrency transfer network on the blockchain reveals that more than 16% transfers of cryptocurrency in Steemit are sent to curators suspected to be bots and also finds the existence of an underlying supply network for the bots, both suggesting a significant misuse of the current reward system in Steemit. Our study is designed to provide insights on the current state of this emerging blockchain-based social media platform including the effectiveness of its design and the operation of the consensus protocols and the reward system.
Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan · 8 authors
Home appliance manufacturers strive to obtain feedback from users to improve their products and services to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model provided by the manufacturer using both the mobile phone and the mobile edge computing (MEC) server. Customers collect data from various home appliances using phones, and then they download and train the initial model with their local data. After deriving local models, customers sign on their models and send them to the blockchain. In case customers or manufacturers are malicious, we use the blockchain to replace the centralized aggregator in the traditional FL system. Since records on the blockchain are untampered, malicious customers or manufacturers' activities are traceable. In the second stage, manufacturers select customers or organizations as miners for calculating the averaged model using received models from customers. By the end of the crowdsourcing task, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL task, we design an incentive mechanism to award participants.
Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that need human intrinsic, such as image annotation. However, there are two limitations in existing crowdsourcing platforms, i.e. non-transparent incentive mechanism and isolated profiles of workers, which harms the interests of both requesters and workers. Meanwhile, Blockchain technology introduces a solution to build a transparent, immutable data model in the Byzantine environment. Moreover, Blockchain systems (e.g. Ethereum) can also support the Tuning-complete script called smart contracts. Thus, we are motivated to use the feature of the transparent data model and smart contract in Blockchain to address the two limitations. Based on the proposed solutions, we have designed a Blockchain based framework which supports foundations of general crowdsourcing platforms. In addition, our framework also has following novel features: (1) it provides the transparent incentive mechanisms; (2) it supports a trusted worker's profile sharing in a cross-platform mode.
Abstract In this paper, the authors propose to increase the efficiency of blockchain mining by using a population-based approach. Blockchain relies on solving difficult mathematical problems as proof-of-work within a network before blocks are added to the chain. Brute force approach, advocated by some as the fastest algorithm for solving partial hash collisions and implemented in Bitcoin blockchain, implies exhaustive, sequential search. It involves incrementing the nonce (number) of the header by one, then taking a double SHA-256 hash at each instance and comparing it with a target value to ascertain if lower than that target. It excessively consumes both time and power. In this paper, the authors, therefore, suggest using an inner for-loop for the population-based approach. Comparison shows that it’s a slightly faster approach than brute force, with an average speed advantage of about 1.67% or 3,420 iterations per second and 73% of the time performing better. Also, we observed that the more the total particles deployed, the better the performance until a pivotal point. Furthermore, a recommendation on taming the excessive use of power by networks, like Bitcoin’s, by using penalty by consensus is suggested.
Mobile crowdsensing(MCS) is an emerging pattern which means task initiators attract mobile users sensing with their own devices by some platforms. MCS could exploit idle resources in low cost, while it has lots of flaws, which impede its developments. First, isolations between different MCS systems leads to wastage of social resources. What's more, current MCS always operate in a centralized way, which causes it vulnerable and unbelievable. Blockchain is a promising technology which could supply a credible and transparent environment. This paper construct a blockchain based MCS market and design smart contract for its operation. In our design, platform breaks isolation by blockchain, task initiators and mobile users manage their tasks by smart contract and bargain price with distributed algorithm. By this way, resource could be exploited better, and the market could be more fair. What's more, the paper analyzes Walrasian Equilibrium (WE) in the market, and details how to deploy MCS in blockchain. Evalution results shows that Equilibrium could be found.
Recent years has witnessed a boom in fog-assisted crowdsensing, which exploits powerful sensing capabilities of various mobile devices or vehicles distributed in large-scale areas to efficiently gather information and make better decisions. However, the fog-assisted crowdsensing system is totally open, which provides the opportunity for malicious individuals or organizations to launch different attacks. In order to cope with the security threats from participants, a blockchain-based crowdsensing framework is proposed, which helps check the authentication of submitted sensor data and resists record tempering. Moreover, a bitcoin-based reward delivery scheme is designed to prevent requesters from denying payments. The sensing capability differences between users are considered in our design. Through security analysis and simulations evaluation, the performance superiority of the proposed framework and reward delivery scheme is demonstrated, in terms of malicious behaviour detection, user utility and sensor data quality.
The Internet of Thing (IoT) is generating an unprecedented volume of data, facilitating the rise of the Data Economy. Under this ecosystem, the IoT data marketplace (IDM) provides an online platform for IoT data trading. Most current IDM solutions are centralized, serving as an intermediary between the data provider and consumer. They support selling and buying data collections (e.g. pre-collected data sets and data streams). However, as these data collections are historically collected without having any priory requirements and preferences given by the data consumer, it is very likely that most of these data collections cannot fulfill data consumer requirements. Redundancy, poor data quality, inaccuracy and limited financial incentives are common problems for existing IDMs, thereby making them financially ineffective. Due to the temporal nature of IoT data, the issue of pre-collection becomes even more critical as it is not trivial to re-collect data from the past.This paper presents a novel framework with a new on-demand market model, namely Decentralized IoT Collectability Data Marketplace (DCDM) model. Difference from the conventional IDM models where merely data collections are considered as a commodity, our model also involves operational factors such as data provider availability, operationality, contextual and geographical location etc. This empowers our framework to trade not only the dataset but rather an ability of the data provider to fulfill the collection task. Our game-changing approach is realized using a combination of Blockchain and Crowdsensing techniques. We first explore the concept of ”Data Collectability” for IDM, then build a model to deploy our approach on IoT context. Furthermore, we identify potential applications and open research challenges.
Truth discovery with crowdsourcing has become increasingly popular in recent years by leveraging the wisdom of crowd to solve complex tasks. So far, many existing crowd-sourcing applications utilize a central server for deployment, which collects and processes data from a crowd of workers. However, this kind of centralized model also introduces security concerns, including data privacy, integrity of processed data, and single point of failure. In this paper, we propose a secure decentralized framework for truth discovery with a privacy-preserving and reliable realization. Instead of relying on the central servers (e.g., public cloud servers)to behave correctly, our framework delegates the data aggregation and processing tasks to distributed entities, whose behaviors are enforced and verified by utilizing the emerging blockchain-based smart contract technology. Meanwhile, as the blockchain lacks support for on-chain data confidentiality, we utilize the privacy-preserving solution and integrate it with blockchain for privacy protection. Moreover, given the decentralized nature of our framework, it also overcomes the limitation of single point of failure. We implement a prototype of our framework using Ethereum blockchain and demonstrate its practical performance.
Mohamad Arafeh, May El Barachi, Azzam Mourad, Fatna Belqasmi
With the emergence of mobile crowdsensing (MCS), we now have the possibility of leveraging the sensing capabilities of mobile devices to collect information and intelligence about cities and events. Despite the promise that MCS brings, this new concept opens the door to a multitude of security and privacy threats and attacks. Indeed, the human involvement in the crowdsensing process and the openness of this process to any participant, render the task of securing MCS environments very challenging. In this work, we propose a Blockchain-based hybrid architecture for the detection and prevention of fake sensing activities in MCS. Our architecture leverages the capabilities of the Blockchain network and introduces a new role to the MCS architecture to ensure the validation of the collected information. Combining both data quality metrics along with behavioral analysis based participants' reliability scoring, our solution is able to detect variations in behavior and quality of contributions. The proposed solution was tested with real life data collected from 200 mobile users, over the span of 2 years, and the results obtained are very promising.
Jian An, Yang He, Xiaolin Gui, Wendong Zhang · 6 authors
With the rapid growth of smart terminals in recent years, crowdsensing which utilizes the human intelligence to solve complicated problems have gained considerable interest and exploit. The majority of the existing crowdsensing systems rely on a trusted third-party platform to complete sensing tasks and collect large-scale data. However, the platform cannot completely ensure trust in the real world. The issues of security and privacy caused by the center platform should not be ignored. In this paper, we propose a decentralized privacy-preserving model based on twice verifications and consensuses of blockchain (TCNS). In the prototype of TCNS, an anonymity strategy which can be verified based on the elliptic curve algorithm is proposed to protect the user identity privacy. Then, we propose a twice consensus mechanism, which ensures that the data can be traced and avoids data from being impersonated, tampered with, and denied. Moreover, we propose a user attribute protection scheme based on the lightweight homomorphic encryption algorithm. Finally, considering various influencing factors comprehensively, TCNS uses fuzzy theories to select the candidate mobile nodes. Further, we implement the prototype with real-world datasets, the experimental analysis of privacy protection and safety shows that TCNS can effectively prevent association analysis attacks and background knowledge attacks. More gratifying, the time overhead for generating a new block is acceptable.
Distributed Ledger Technologies (DLT), of which Blockchain is a popular example, are increasingly becoming an integral feature of many modern systems.While cryptocurrencies are a common motivating example, and they drive systems such as Bitcoin, DLT has many other uses across industries including Health Care, Supply Chain, IoT, and Finance among others.One of the key concepts that make DLT appealing is the ability for large-scale systems that do not trust each other to reach consensus and share a commonly verifi able ledger, in order to track resources, changes to system-wide data, and other artifacts.Cryptocurrencies are one type of resource that can be tracked, but DLT has been used for energy, pharmaceuticals, and many other domains.
Blockchains - with their inherent properties of transaction transparency, distributed consensus, immutability and cryptographic verifiability - are increasingly seen as a means to underpin innovative products and services in a range of sectors from finance through to energy and healthcare. Discussions, too often, make assertions that the trustless nature of blockchain technologies enables and actively promotes their suitability - there being no need to trust third parties or centralised control. Yet humans need to be able to trust systems, and others with whom the system enables transactions. In this paper, we highlight that understanding this need for trust is critical for the development of blockchain-based systems. Through an online study with 125 users of the most well-known of blockchain based systems - the cryptocurrency Bitcoin - we uncover that human and institutional aspects of trust are pervasive. Our analysis highlights that, when designing future blockchain-based technologies, we ought to not only consider computational trust but also the wider eco-system, how trust plays a part in users engaging/disengaging with such eco-systems and where design choices impact upon trust. From this, we distill a set of guidelines for software engineers developing blockchain-based systems for societal applications.
Blockchain technology has found a great number of applications, from banking to the Internet of Things (IoT). However, it has not yet been envisioned whether and which problems in Software Engineering (SE) Blockchain technology could solve. In this paper, we coin this field "Blockchain-based Software Engineering" and exemplify how Blockchain technology could solve two core SE problems: Continuous Integration (CI) Services such as Travis CI and Package Managers such as apt-get. We believe that Blockchain technology could help (1) democratize and professionalize Software Engineering infrastructure that currently relies on free work done by few volunteers, (2) improve the quality of artifacts and services, and (3) increase trust in ubiquitously used systems like GitHub or Travis CI.
Kapil Singi, Vikrant Kaulgud, R. P. Jagadeesh Chandra Bose, Sanjay Podder
The software development life cycle (SDLC) starts with business and functional specifications signed with a client. In addition to this, the specifications also capture policy / procedure / contractual / regulatory / legislation / standard compliances with respect to a given client industry. The SDLC must adhere to service level agreements (SLAs) while being compliant to development activities, processes, tools, frameworks, and reuse of open-source software components. In today's world, global software development happens across geographically distributed (autonomous) teams consuming extraordinary amounts of open source components drawn from a variety of disparate sources. Although this is helping organizations deal with technical and economic challenges, it is also increasing unintended risks, e.g., use of a non-complaint license software might lead to copyright issues and litigations, use of a library with vulnerabilities pose security risks etc. Mitigation of such risks and remedial measures is a challenge due to lack of visibility and transparency of activities across these distributed teams as they mostly operate in silos. We believe a unified model that non-invasively monitors and analyzes the activities of distributed teams will help a long way in building software that adhere to various compliances. In this paper, we propose a decentralized CAG - Compliance Adherence and Governance framework using blockchain technologies. Our framework (i) enables the capturing of required data points based on compliance specifications, (ii) analyzes the events for non-conformant behavior through smart contracts, (iii) provides real-time alerts, and (iv) records and maintains an immutable audit trail of various activities.
Crowdsourcing is a booming technique that enables participants to exchange data directly, thus making it possible to answer latency-sensitive service requests and relieve the burden of core networks. With some incentives, providers compete to furnish service requests, thus pledging the quality of experience (QoE) for requestors. However, the decentralized communication in crowdsourcing increases the probability of information tapering. Furthermore, providers' arbitrary selection of the requests poses great threat to the efficient and profitable service provision for the requestors. To deal with these challenges, we propose a blockchain-powered crowdsourcing method, named BPCM, while considering the privacy preservation in mobile environment. Specifically, a mobile crowdsourcing framework based on blockchain is designed first to preserve the privacy of the participants and keep the integrity of the service request and provision. Then, density-based spatial clustering of applications with noise (DBSCAN) and improved dynamic programming (IDP) are adopted to cluster the requestors and generate service strategies, respectively. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) are utilized to select the optimal strategy that achieves the tradeoffs among maximizing the service time, increasing the profits, and reducing the energy consumption for the providers. Finally, comprehensive experiments are conducted to verify the accuracy and effectiveness of BPCM.
BACKGROUND: Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE: This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS: The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS: A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS: DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.
This paper presents an empirical analysis of Steemit, a key representative of the emerging incentivized social media platforms over Blockchains, to understand and evaluate the actual level of decentralization and the practical effects of cryptocurrency-driven reward system in these modern social media platforms. Similar to Bitcoin, Steemit is operated by a decentralized community, where 21 members are periodically elected to cooperatively operate the platform through the Delegated Proof-of-Stake (DPoS) consensus protocol. Our study performed on 539 million operations performed by 1.12 million Steemit users during the period 2016/03 to 2018/08 reveals that the actual level of decentralization in Steemit is far lower than the ideal level, indicating that the DPoS consensus protocol may not be a desirable approach for establishing a highly decentralized social media platform. In Steemit, users create contents as posts which get curated based on votes from other users. The platform periodically issues cryptocurrency as rewards to creators and curators of popular posts. Although such a reward system is originally driven by the desire to incentivize users to contribute to high-quality contents, our analysis of the underlying cryptocurrency transfer network on the blockchain reveals that more than 16% transfers of cryptocurrency in Steemit are sent to curators suspected to be bots and also finds the existence of an underlying supply network for the bots, both suggesting a significant misuse of the current reward system in Steemit. Our study is designed to provide insights on the current state of this emerging blockchain-based social media platform including the effectiveness of its design and the operation of the consensus protocols and the reward system.
With the development of smart vehicles and computation-intensive vehicular applications, it is a challenge to maintain high performance for vehicles with scarce local computational resources. Mobile Edge Computing (MEC) is a computing paradigm with high potential to improve vehicular services by offloading computation-intensive tasks to the MEC servers. However, as the computational resources of MEC servers are limited, parking lots (PLs) having abundant idle computational resources should be utilized. We introduce a new computing paradigm, named by Vehicular Parked-Edge Computing (VPEC). We formulate a three-stage contract-stackelberg offloading incentive mechanism to describe this problem. The PLs are classified into different types according to their idle computational resources, and parking lot agent (PLA) offers different contracts to different types of PLs. The optimal problem is designed to maximize the utilities of vehicles, operator and PLA. We use backward induction method to solve this three-stage problem, and give the closed-form expressions of the optimal strategies for each stage. Simulation results demonstrate the feasibility of the proposed incentive mechanism and reveal the changing trend of optimal strategies in each stage when traffic density changes.
Mohamed Ridda Laouar, Zaineb Touati Hamad, Sean B. Eom
The potential of urban planning to solve environmental problems in general and manage waste, in particular, is important because the illegal dumping of solid waste is one of the events related to illegal waste treatment activities. The waste management process is very poor, especially when it comes to confirming the correct destination for the delivery of waste. In this work, we are proposing a chain-based approach to waste tracking to enable waste data reporting in a single system. The blockchain is a technology that has already proven itself in the financial sector. It securely records transactions in a large, time-stamped ledger using the proof of work algorithm (PoW), and allows us to write smart contracts on the ethereum network. The application of this technology to the waste sector will enable reliable, transparent and secure recording of all waste movements, thus enabling waste to be traced from source to treatment and disposal. Users will be able to import and export data through a user interface offering different levels of functionality and access. It will also identify and act on illegal wasteful crimes.
Blockchain has created a revolution in digital networking by using distributed storage, cryptographic algorithms, and smart contracts. Many areas are benefiting from this technology, including data integrity and security, as well as authentication and authorization. Internet of Things (IoTs) networks often suffers from such security issues, which is slowing down wide-scale adoption. In this paper, we describe the employing of blockchain technology to construct a decentralized platform for storing and trading information in the air-to-ground IoT heterogeneous network. To allow both air and ground sensors to participate in the decentralized network, we design a mutual-benefit consensus process to create uneven equilibrium distributions of resources among the participants. We use a Cournot model to optimize the active density factor set in the heterogeneous air network and then employ a Nash equilibrium to balance the number of ground sensors, which is influenced by the achievable average downlink rate between the air sensors and the ground supporters. Finally, we provide numerical results to demonstrate the beneficial properties of the proposed consensus process for air-to-ground networks and show the maximum active sensor's density utilization of air networks to achieve a high quality of service.
Summary The growth of counterfeit goods has plagued the international community for decades. Nowadays, the battle against counterfeiting remains a significant challenge. Most of the current anti‐counterfeiting systems are centralized. Motivated by the evolution of blockchain technology, we propose (Block‐Supply), a decentralized anti‐counterfeiting supply chain that exploits NFC and blockchain technologies. This paper also proposes a new truly decentralized consensus protocol that, unlike most of the existing protocols, does not require PoW and randomly employs a different set of different size of validators each time a new block is proposed. Our protocol utilizes a game theoretical model to analyze the risk likelihood of the block's proposing nodes. This risk likelihood is used to determine the number of validators involved in the consensus process. Additionally, the game model enforces the honest consensus nodes' behavior by rewarding honest players and penalizing dishonest ones. Our protocol utilizes a novel, decentralized, dynamic mapping between the nodes that participate in the consensus process. This mapping ensures that the interaction between these nodes is executed anonymously and blindly. This way of mapping withstands many attacks that require knowing the identities of the participating nodes in advance, such as DDoS, Bribery, and Eclipse attacks.