4. DSP Layer - Data Service Platform

4.1 Overview

The Data Service Platform (DSP) serves as the primary commercial interface of the xKnown ecosystem, enabling trusted, privacy-preserving, and verifiable data exchange between contributors, AI developers, enterprises, and model builders. Acting as both a data marketplace and an AI asset brokerage layer, DSP bridges raw data supply with structured AI demand through a multi-sided platform architecture.

The DSP layer supports three major roles:

• C-side (Data Contributors): Individuals upload their personal data or perform micro-data tasks such as annotation, labeling, or transcription. Uploaded data undergoes initial quality screening by AI Agents to ensure minimal acceptance thresholds.

• B-side (Enterprise & AI Developers): Organizations can submit customized data acquisition requests (Call-for-Data Services), issue real-time crowdsourcing tasks, and subscribe to verified datasets pre-qualified by xKnown's agent-based valuation system.

• AI Marketplace: An open marketplace for trading AI assets, including datasets, pre-trained models, AI Agents, and workflow pipelines. Developers and AI builders may purchase clean, high-value datasets or offer proprietary models, with flexible licensing and monetization structures. DSP may also collaborate as a syndication partner to third-party AI marketplaces such as Sahara AI.

4.2 Privacy-Preserving Data Valuation Protocol

Given the sensitivity of personal data, the DSP integrates multiple privacy-preserving valuation techniques to ensure that:

• Agent-based valuation can occur without raw data leakage.

• Data contributors retain sovereignty over their private content.

• Valuation results are still transparent, auditable, and usable for transaction settlement.

The core privacy-enhanced valuation stack combines:

• Differential Privacy (DP)^[4]: To inject calibrated noise during valuation aggregation, ensuring individual record privacy is maintained.

• Homomorphic Encryption (HE)^[5]: Enables AI Agents to compute partial valuation scores directly over encrypted feature vectors, without decrypting sensitive inputs.

• Secure Multi-Party Computation (SMPC)^[6]: Supports joint valuation between multiple stakeholders without full data exposure.

• Shapley-Value Inspired DP Extensions^[7]: Leverages advanced Shapley value^[8][9] calculations, incorporating differential privacy to estimate marginal data contributions under privacy constraints, allowing buyers to assess expected value prior to direct access.

In practice, B-side buyers may access summary valuation scores derived from encrypted pipelines, but cannot retrieve raw data unless authorized through contractual settlement.

4.3 Fully Verifiable Batch Data Trading Protocol

To support scalable enterprise-grade data transactions, DSP implements a zero-knowledge-based settlement mechanism that allows bulk dataset exchanges between multiple sellers and institutional buyers. The full protocol proceeds as follows:

(1) Seller-Side Dataset Preparation

Each seller selects private data , encrypts its location address using a secret key , and uploads encrypted data to IPFS.

The seller generates a zk-SNARK proof demonstrating:

• Ownership of matching IPFS hash ​.

• Encryption consistency of address ​ via ​.

• Compliance with valuation function F(), where F() reflects the xKnown Agent's computed value score.

(2) Buyer-Side Verification

The buyer receives encrypted metadata:

zk-SNARK proofs are verified to confirm sellers’ ownership and compliance without revealing raw content.

(3) Smart Contract Escrow Deployment

The buyer commits payment allocations ​ into smart contracts, conditional on the seller submitting valid decryption keys.

If valid keys are submitted and verified against ​, funds are released. Otherwise, funds are refunded after a timeout.

(4) Key Release & Payment Settlement

Sellers submit their secret keys to the smart contract.

The contract validates successful decryption, ensuring address consistency, and distributes payments.

(5) Secure Data Delivery

Buyers decrypt using ​, retrieve private data locations, and access full datasets for downstream AI training.

This mechanism enables:

• Trustless multi-party batch transactions.

• Decentralized settlement and escrow through blockchain.

• Full data privacy preservation until transaction finalization.

• Flexible pricing structures linked directly to individual valuation scores.

4.4 DSP Platform Positioning within xKnown

The DSP layer functions not only as a marketplace but as a complete trust-minimized data brokerage protocol stack:

• Fully integrated with xKnown’s upstream AI Agent valuation pipeline;

• Powered by encrypted valuation models that balance privacy and transparency;

• Enabling cross-border, multi-party institutional data commerce at scale;

• Providing composability for enterprise-specific marketplaces, licensing syndication, and cross-chain data liquidity.