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, valid hash) adversaries, VeriX-Anon correctly detected deviations in 11 of 12 scenarios. No single layer achieved this alone. The XAI layer was the only mechanism that caught the Approximate adversary, succeeding on Adult and Bank but failing on the severely imbalanced Diabetes dataset where class imbalance suppresses the SHAP signal, confirming the need for adaptive thresholding. An 11-point k-sweep showed Target-Driven anonymization preserves significantly more utility than Blind anonymization (Wilcoxon $p = 0.000977$, Cohen's $d = 1.96$, mean F1 gap $+0.1574$). Client-side verification completes under one second at one million rows. The threat model covers three empirically evaluated profiles and one theoretical profile (Informed Attacker) aware of trap embedding but unable to defeat the cryptographic salt. Sentinel evasion probability ranges from near-zero for balanced datasets to 0.52 for imbalanced ones, a limitation the twin layer compensates for in every tested scenario.

翻译：组织日益将隐私敏感的数据转换外包给云服务提供商，但尚缺乏实用机制让数据所有者验证约定算法是否被忠实执行。VeriX-Anon是一种面向外包目标驱动k-匿名化的多层验证框架，结合了三种正交机制：基于认证决策树的Merkle风格哈希的确定性验证、基于随机森林决策边界附近的边界哨兵与具有加密标识符的精确重复孪生记录的概率性验证，以及基于可解释AI指纹的效用性验证——通过Wasserstein距离比较匿名化前后的SHAP值分布。在三个跨领域数据集上，针对懒惰型（丢弃5%记录）、愚钝型（随机拆分、伪造哈希）和近似型（随机拆分、有效哈希）攻击者进行的评估表明，VeriX-Anon在12种场景中正确检测到11种偏差。单一层无法独立实现此效果。XAI层是唯一能捕获近似型攻击者的机制，在Adult和Bank数据集上成功，但在严重类别不平衡的Diabetes数据集上失败——类别不平衡抑制了SHAP信号，证实了自适应阈值的必要性。11点k值扫描显示，目标驱动匿名化比盲目匿名化保留显著更高的效用（Wilcoxon p=0.000977，Cohen's d=1.96，平均F1差距+0.1574）。客户端验证在百万行数据规模下完成时间不超过1秒。威胁模型涵盖三个经验评估剖面和一个理论剖面（知情攻击者），该攻击者知晓陷阱嵌入但无法破解加密随机盐值。哨兵规避概率范围从平衡数据集的接近零到不平衡数据集的0.52，而孪生层在每次测试场景中均弥补了这一局限性。