We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities - data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12-peer non optimized blockchain network and roughly 30 models per second in a 24 peer network.
Blockchain has the potential to revolutionize the way we store, use, and process data. Information on most blockchains can be viewed by every node hosting the blockchain, which means that most blockchains cannot handle private data. Decentralized databases exist that guarantee privacy by encrypting user data with the user's private key, but this prevents easy data sharing. However, in many real world applications, from student data to medical records, it is desirable that user data is anonymously searchable. In this paper we present a novel system that gives users ownership over their data whi…
Quartierstrom creates a peer-to-peer marketplace for locally generated solar power. The marketplace is implemented as a smart contract on a permissioned blockchain governed by all prosumers. Two privacy-by-design concepts are presented which guarantee that the users individual load profile is not leaked to any third party despite using a blockchain. The first approach leverages UTXO based coin mixing protocols in combination with an account-based on-chain smart contract. The second approach relies on an off-chain smart contract running in trusted execution environments.
A method for timestamping, anchoring and certification of a virtually unlimited amount of data in one or more blockchains, focusing on scalability and cost-effectiveness while ensuring existence, integrity and ownership by using cryptographic proofs that are independently verifiable by anyone in the world without disclosure of the original data and without the intervention of the certifying party.
We provide a UTXO model of blockchain transactions that is able to represent both credit and debt on the same blockchain. Ordinarily, the UTXO model is solely used to represent credit and the representation of credit and debit together is achieved using the account model because of its support for balances. However, the UTXO model provides superior privacy, safety, and scalability when compared to the account model. In this work, we introduce a UTXO model that has the flexibility of balances with the usual benefits of the UTXO model. This model extends the conventional UTXO model, which repres…
SRM Institute Of Science And Technology（SRM科学技术研究所）
A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the proce…
The unique properties of blockchain enable central requirements of distributed secure logging: Immutability, integrity, and availability. Especially when providing transparency about data usages, a blockchain-based secure log can be beneficial, as no trusted third party is required. Yet, with data governed by privacy legislation such as the GDPR or CCPA, the core advantage of immutability becomes a liability. After a rightful request, an individual's personal data need to be rectified or deleted, which is impossible in an immutable blockchain. To solve this issue, we exploit a legal property o…
To promote the benefits of the Internet of Things (IoT) in smart communities and smart cities, a real-time data marketplace middleware platform, called the Intelligent IoT Integrator (I3), has been recently proposed. While facilitating the easy exchanges of real-time IoT data streams between device owners and third-party applications through the marketplace, I3 is presently a monolithic, centralized platform for a single community. Although the service oriented architecture (SOA) has been widely adopted in the IoT and cyber-physical systems (CPS), it is difficult for a monolithic architecture …
The increasing amount of personal data is raising serious issues in the context of privacy, security, and data ownership. Entities whose data are being collected can benefit from mechanisms to manage the parties that can access their data and to audit who has accessed their data. Consent management systems address these issues. We present Consentio, a scalable consent management system based on the Hyperledger Fabric permissioned blockchain. The data management challenge we address is to ensure high throughput and low latency of endorsing data access requests and granting or revoking consent. …
Hong Kong University of Science and Technology（香港科技大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Southern California（南加州大学）
We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to ma…
Service Level Agreements (SLA) are employed to ensure the performance of Cloud solutions. When a component fails, the importance of logs increases significantly. All departments may turn to logs to determine the cause of the issue and find the party at fault. The party at fault may be motivated to tamper with the logs to hide their role. We argue that the critical nature of Cloud logs calls for immutability and verification mechanism without the presence of a single trusted party. This paper proposes such a mechanism by describing a blockchain-based log storage system, called Logchain, which c…
Imperial College London（伦敦帝国学院）；Vodafone（沃达丰）；Technical University of Munich（慕尼黑工业大学）；ConsenSys Health
Federated Learning harnesses data from multiple sources to build a single model. While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question. In this paper we explore how Blockchains (in particular Ethereum) can be used to determine the evolving ownership of a model trained with Federated Learning. Firstly, we use the step-by-step evaluation metric to assess the relative contributivities of participants in a Federated Learning process. Next, we introdu…
University of California Irvine（加州大学欧文分校）；Texas A&M University（得克萨斯农工大学）；Theta Labs Inc.（西塔实验室公司）；Camford Capital（坎福德资本）；University of Southern California（南加州大学）；FedML Inc.（FedML公司）
We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers …
ETH Zurich（苏黎世联邦理工学院）；IST Austria（奥地利科学技术研究所）；Swisscom（瑞士电信）
It is safe to assume that, for the foreseeable future, machine learning, especially deep learning will remain both data- and computation-hungry. In this paper, we ask: Can we build a global exchange where everyone can contribute computation and data to train the next generation of machine learning applications? We present an early, but running prototype of DataBright, a system that turns the creation of training examples and the sharing of computation into an investment mechanism. Unlike most crowdsourcing platforms, where the contributor gets paid when they submit their data, DataBright pays …
Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with da…
Climate change and global warming are the significant challenges of the new century. A viable solution to mitigate greenhouse gas emissions is via a globally incentivized market mechanism proposed in the Kyoto protocol. In this view, the carbon dioxide (or other greenhouse gases) emission is considered a commodity, forming a carbon trading system. There have been attempts in developing this idea in the past decade with limited success. The main challenges of current systems are fragmented implementations, lack of transparency leading to over-crediting and double-spending, and substantial trans…
Non-fungible tokens (NFTs) are unique digital items with blockchain managed ownership. Ethereum blockchain based smart contract created the environment for NFTs (ERC721) to reach its one of the most important future application domains. Non fungible tokens got more attention when the market saw record breaking sales in 2021. Virtually anything of value can be traced and traded on the blockchain network by minting them as NFTs. NFTs provide the users with a decentralized proof of ownership representation, as every transaction and trade of NFTs gets recorded in the Ethereum network blocks. The v…
SPEEDEX is a decentralized exchange (DEX) that lets participants securely trade assets without giving any single party undue control over the market. SPEEDEX offers several advantages over prior DEXes. It achieves high throughput -- over 200,000 transactions per second on 48-core servers, even with tens of millions of open offers. SPEEDEX runs entirely within a Layer-1 blockchain, and thus achieves its scalability without fragmenting market liquidity between multiple blockchains or rollups. It eliminates internal arbitrage opportunities, so that a direct trade from asset $\mathcal{A}$ to asset…
As Machine Learning (ML) models are becoming increasingly complex, one of the central challenges is their deployment at scale, such that companies and organizations can create value through Artificial Intelligence (AI). An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. However, the problem of valuation of models, as well the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the litera…
Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a centralized third-party entity to negotiate between data consumers and data providers, which is inefficient and insecure on a large scale. In comparison, a decentralized approach based on distributed ledger technologies (DLT) enables data tra…
