Blockchain-Assisted Federated Attribution and Incentive Mechanisms for Transparent Cross-Platform Advertising Measurement in AI-Driven Commerce Networks
Keywords:
blockchain, federated learning, advertising attribution, incentive mechanisms, cross-platform measurement, differential privacy, smart contracts, AI commerce, transparency, privacy preservationAbstract
The proliferation of artificial intelligence-driven commerce networks has intensified the complexity of cross-platform advertising measurement, where attribution of consumer actions across multiple digital touchpoints remains fraught with data fragmentation, privacy constraints, and lack of transparency. Existing centralized attribution models suffer from single points of failure, opaque algorithmic decision-making, and misaligned incentives among advertisers, platforms, and publishers. This paper proposes a comprehensive system-level framework that integrates blockchain technology with federated learning principles to enable transparent, privacy-preserving attribution and incentive mechanisms for cross-platform advertising. The architecture combines on-chain immutable logging of attribution events with off-chain federated computation that preserves user-level data locality. An adaptive differential privacy budget allocation mechanism ensures rigorous privacy guarantees while maintaining utility for downstream measurement tasks. A tokenized incentive layer rewards honest contributions and penalizes malicious behavior, aligning the economic interests of all stakeholders. The paper examines structural trade-offs among transparency, privacy, scalability, and fairness, drawing on cross-domain comparisons with decentralized finance and supply chain provenance systems. Governance considerations, including smart contract upgradeability, oracle reliability, and regulatory compliance under frameworks such as the General Data Protection Regulation, are discussed. Case illustrations from social commerce and programmatic advertising demonstrate the feasibility of the approach. The paper concludes by outlining future research directions, including integration with large language models for policy generation and zero-knowledge proofs for verifiable computation. The proposed framework offers a pathway toward a more equitable and auditable advertising ecosystem in AI-driven commerce.
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