arXiv:2604.21211v2 Announce Type: replace Abstract: Current text anonymization evaluation relies on span-based metrics that fail to capture what an adversary could actually infer, and assumes a single data subject, ignoring multi-subject scenarios. To address these limitations, we present SPIA (Subject-level PII Inference Assessment), the first benchmark that shifts the unit of evaluation from text spans to individuals, comprising 675 documents across legal and online domains with novel subject-level protection metrics. Extensive experiments show that even when over 90% of PII spans are masked
The increasing sophistication of AI models and the critical need for privacy protection in large datasets are driving the development of more robust anonymization techniques.
This benchmark addresses a significant gap in evaluating data anonymization, moving beyond superficial metrics to assess true privacy protection at the individual level, which is critical for legal, ethical, and commercial AI applications.
The standard for assessing text anonymization shifts from merely masking spans of text to evaluating the actual inferability of an individual from a dataset, leading to more secure data practices and potentially new compliance standards.
- · Privacy-focused AI developers
- · Data privacy compliance solutions
- · Legal and healthcare sectors handling sensitive data
- · Individuals whose personal data is being anonymized
- · Developers relying on weak anonymization methods
- · Organizations with inadequate data privacy protocols
Improved anonymization techniques will lead to higher quality and safer datasets for AI training and deployment.
Stricter privacy standards could slow down some AI development if robust anonymization proves difficult or computationally expensive.
Enhanced trust in anonymized data could accelerate the adoption of AI in highly regulated sectors, creating new market opportunities.
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Read at arXiv cs.CL