VeriX-Anon: A Multi-Layered Framework for Mathematically Verifiable Outsourced Target-Driven Data Anonymization
Miit Daga, Swarna Priya Ramu

TL;DR
VeriX-Anon is a comprehensive multi-layered framework enabling clients to verify outsourced data anonymization, combining cryptographic, probabilistic, and AI-based methods to detect deviations across diverse adversaries.
Contribution
It introduces a novel multi-layered verification approach for outsourced Target-Driven k-anonymization, integrating cryptographic hashes, boundary sentinel checks, and explainable AI fingerprinting.
Findings
VeriX-Anon detects deviations in 11 of 12 tested scenarios.
The XAI layer uniquely detects approximate adversaries, especially on balanced datasets.
Client-side verification completes in under one second for one million rows.
Abstract
Organisations increasingly outsource privacy-sensitive data transformations to cloud providers, yet no practical mechanism lets the data owner verify that the contracted algorithm was faithfully executed. VeriX-Anon is a multi-layered verification framework for outsourced Target-Driven k-anonymization combining three orthogonal mechanisms: deterministic verification via Merkle-style hashing of an Authenticated Decision Tree, probabilistic verification via Boundary Sentinels near the Random Forest decision boundary and exact-duplicate Twins with cryptographic identifiers, and utility-based verification via Explainable AI fingerprinting that compares SHAP value distributions before and after anonymization using the Wasserstein distance. Evaluated on three cross-domain datasets against Lazy (drops 5 percent of records), Dumb (random splitting, fake hash), and Approximate (random splitting,…
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