data marketplace operators

Abstract: Data trading markets that span industries and regions are expanding, but the implicit trust that supports data use within an organization does not carry over to cross-domain exchange. This paper examines the Data Credit Guarantee Organization (DCGO), a proposal to institutionalize trust through third-party verification and certification, and asks what it means to trust data, what such certification can support, and under what conditions DCGO outperforms existing trading models. We decompose trust in data into four layers: authenticity of entity, authenticity and integrity of data, rights and conditions of use, and history and auditability. A third party can certify authenticity but not the correctness of content, and DCGO is positioned as a trust anchor spanning existing marketplaces and data spaces. We then extend the Law Merchant model with three data-specific properties, namely imperfect ex-post detection, cheap pseudonyms, and non-rivalry of harm, and compare bilateral trading with DCGO by agent-based simulation. DCGO keeps the buyer's payoff near its theoretical upper bound regardless of the market environment, whereas bilateral trading loses even when fraud is always detected. Identity verification proves to be a prerequisite for record sharing, and pre-verification is the only function that limits the spread of harm. On the seller side, honest sellers join voluntarily whenever the price premium exceeds the fee. Lock-in and a critical mass for diffusion, however, call for fee governance and support for entry.

Abstract: We consider a data seller who designs pricing mechanisms over multiple datasets to maximize revenue from budget-constrained buyers. The seller offers multiple datasets and assigns each a pricing function that maps the quantity purchased to a total payment. The goal is to design these pricing functions to maximize revenue, anticipating that buyers---who trade off accuracy gains against cost---choose bundles optimally subject to their budget constraints. Prior work [Chaudhury et al., 2026] studies such optimal pricing under the restriction that each dataset is assigned a linear price, and shows that computing optimal linear prices is computationally intractable. In contrast, we allow each dataset to be priced via a general function and show that this additional flexibility can not only increase the revenue but also restore tractability, yielding a surprising simultaneous improvement in economic performance and computational efficiency. Even when pricing functions are only required to be monotone and lower-continuous, optimal pricing admits a highly structured and simple form: each pricing function is piecewise linear and convex (PLC), and the optimal solution can be computed in polynomial time. Moreover, the total number of kinks across all pricing functions is bounded by the number of buyers. Consequently, when datasets significantly outnumber buyers, most pricing functions are effectively linear. We further empirically study the structure of optimal pricing by analyzing the number of kinks and the revenue gap between optimal nonlinear pricing and optimal linear pricing on simulations generated from a real dataset.

Abstract: Providers of continuously produced, commercially valuable data -- sensor streams, telemetry, and other feeds sold as machine-learning training material -- cannot observe whether delivered data is actually used, while data that keeps flowing to inactive consumers enlarges the leakage surface without producing value. We present Attestream, a blockchain-based architecture for intermittently delivered dataset streams that couples continued delivery to verifiable usage reporting. Every lifecycle event -- dataset preparation, dual-signed delivery, derivative creation (e.g., a model), and derivative distribution -- is appended to an on-chain registry as a non-repudiable, mutually linked lifecycle record. The mechanism requires provable transfer, not tokenization: plain contract storage, ERC-721 tokens, and anchored off-chain receipts are interchangeable representations of the same protocol. A usage-aware gate suspends a consumer's stream when no derivative-creation record is registered within a reporting window; evaluated lazily inside the next delivery transaction, monitoring adds no dedicated transactions. A modality-pluggable fingerprinting layer binds any leaked copy to the dual-signed delivery record of the responsible consumer, instantiated for tabular/geospatial records, images, and documents. We implement the registry as a Solidity contract with EIP-712 dual signatures and evaluate it: a full lifecycle round costs 657k gas with plain records ($0.13 on rollups; ERC-721 tokenization adds ~30k gas per record), and over a 50-consumer pool leak attribution reaches 100% from 40 leaked table rows under moderate noise, survives JPEG recompression to quality 30, and tolerates paraphrase rates up to 30% for documents.