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<p>The transparency of blockchain technology creates a fundamental challenge for competitive NFT market analysis: while all transactions are publicly visible, the analytical insights derived from this data represent valuable strategic intelligence requiring protection. This paper introduces MultiPriNTF, a privacy-preserving analytical service that enables multiple institutional investors to analyze NFT markets through shared infrastructure without revealing their proprietary valuation strategies or portfolio interests to service providers, competitors, or external observers. Our approach combines Variational Autoencoders with Transformers to integrate visual and transaction data while employing differential privacy mechanisms that prevent adversaries from extracting strategic patterns from analytical outputs. We do not attempt to hide public blockchain data but rather protect query patterns and analytical focus, ensuring that observers cannot determine which NFTs specific analysts consider valuable or which correlations drive their investment decisions. Experimental validation on 167,492 CryptoPunk transactions demonstrates that MultiPriNTF achieves a market efficiency score of 0.85, improving upon existing methods by 18%, while maintaining formal privacy guarantees with privacy budget <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varepsilon &lt; 0.5\)</EquationSource> </InlineEquation> against membership inference and pattern extraction attacks. The system processes pre-indexed blockchain data at 832 transactions per second with 99.98% parameter efficiency, though we acknowledge that real-world deployment requires integration with blockchain indexing infrastructure. Our contribution enables competitive NFT market analysis where multiple institutional participants can utilize shared analytical services to derive sophisticated insights from public data without compromising their analytical strategies. While evaluation remains limited to CryptoPunks and assumes pre-processed data availability, MultiPriNTF demonstrates the feasibility of privacy-preserving analytics in transparent blockchain ecosystems. To promote reproducibility and transparency in our research, the complete MultiPriNTF codebase, model implementations, and anonymized dataset are publicly accessible to facilitate independent verification and encourage further advancements in privacy-preserving NFT market analysis.</p>

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Multiprintf: privacy-preserving multimodal fusion for scalable NFT market analysis

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Kombou Victor,
  • Qi Xia,
  • Wei Zhang,
  • Hu Xia,
  • Jianbin Gao,
  • Kuiche Sop Brinda Leaticia

摘要

The transparency of blockchain technology creates a fundamental challenge for competitive NFT market analysis: while all transactions are publicly visible, the analytical insights derived from this data represent valuable strategic intelligence requiring protection. This paper introduces MultiPriNTF, a privacy-preserving analytical service that enables multiple institutional investors to analyze NFT markets through shared infrastructure without revealing their proprietary valuation strategies or portfolio interests to service providers, competitors, or external observers. Our approach combines Variational Autoencoders with Transformers to integrate visual and transaction data while employing differential privacy mechanisms that prevent adversaries from extracting strategic patterns from analytical outputs. We do not attempt to hide public blockchain data but rather protect query patterns and analytical focus, ensuring that observers cannot determine which NFTs specific analysts consider valuable or which correlations drive their investment decisions. Experimental validation on 167,492 CryptoPunk transactions demonstrates that MultiPriNTF achieves a market efficiency score of 0.85, improving upon existing methods by 18%, while maintaining formal privacy guarantees with privacy budget \(\varepsilon < 0.5\) against membership inference and pattern extraction attacks. The system processes pre-indexed blockchain data at 832 transactions per second with 99.98% parameter efficiency, though we acknowledge that real-world deployment requires integration with blockchain indexing infrastructure. Our contribution enables competitive NFT market analysis where multiple institutional participants can utilize shared analytical services to derive sophisticated insights from public data without compromising their analytical strategies. While evaluation remains limited to CryptoPunks and assumes pre-processed data availability, MultiPriNTF demonstrates the feasibility of privacy-preserving analytics in transparent blockchain ecosystems. To promote reproducibility and transparency in our research, the complete MultiPriNTF codebase, model implementations, and anonymized dataset are publicly accessible to facilitate independent verification and encourage further advancements in privacy-preserving NFT market analysis.