PrivaMod: Uncertainty-Aware Multimedia Fusion with Privacy Guarantees for NFT Visual and Transaction Analysis

Abstract

Non-fungible token (NFT) markets present a dual analytical challenge: integrating heterogeneous data modalities (high-dimensional visual features and discrete transaction sequences) while preserving privacy for sensitive wallet addresses and trading strategies. Current approaches analyze visual attributes or transaction patterns in isolation, missing critical value drivers from cross-modal interactions. Meanwhile, existing multimodal techniques lack formal privacy guarantees, exposing participants to inference attacks. This article introduces PrivaMod, a privacy-preserving Bayesian framework that addresses these limitations through uncertainty-aware multimodal fusion. Our approach implements precision-weighted Bayesian fusion that dynamically adjusts modality contributions based on quantified uncertainty levels, while integrating Rényi Differential Privacy throughout the pipeline via calibrated noise injection and adaptive gradient clipping. Evaluated on 167,492 CryptoPunk transactions, PrivaMod achieves a market efficiency score of 0.874 and R 2 of 0.912, outperforming existing methods by 13.4% through superior cross-modal integration while maintaining strong privacy guarantees ( \(\varepsilon\) = 0.08, \(\delta\) = 1e-5) with membership inference attack success rates near random guessing (53.4%). The system demonstrates that privacy-preserving techniques can enhance rather than compromise analytical performance, establishing a foundation for responsible market analysis. To ensure reproducibility, we release our code, preprocessed datasets, and model checkpoints with detailed documentation and scripts to replicate all experiments. PrivaMod is available at https://github.com/kvjunior/PrivaMod/blob/main/README.md .