metadata
license: mit
task_categories:
- text-generation
- token-classification
language:
- en
tags:
- privacy
- anonymization
- pii
- ner
- text-rewriting
- benchmark
size_categories:
- 100K<n<1M
pretty_name: 'SAHA-AL: PII Anonymization Benchmark'
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: original_text
dtype: string
- name: anonymized_text
dtype: string
- name: entities
list:
- name: text
dtype: string
- name: type
dtype: string
- name: start
dtype: int32
- name: end
dtype: int32
splits:
- name: train
num_examples: 113133
- name: validation
num_examples: 3600
- name: test
num_examples: 3600
SAHA-AL: PII Anonymization Benchmark
SAHA-AL is a benchmark for training and evaluating text anonymization systems. It goes beyond detection accuracy by evaluating anonymization as a system under attack — measuring adversarial re-identification risk, contextual privacy leakage, and a formalized privacy-utility tradeoff.
Key Features
- 3 evaluation tasks: PII detection, text anonymization quality, and adversarial privacy risk
- 11 metrics spanning leakage, utility, format preservation, and multi-vector attack resistance
- 8 baseline systems evaluated: 3 rule-based (Regex, spaCy, Presidio) and 5 fine-tuned seq2seq models
- Privacy-Utility Score (PUS): A parameterized framework for navigating the privacy-utility tradeoff
- Failure taxonomy: Diagnostic error classification (full leak, boundary error, format break, context retention)
Dataset
| Split | Records | Entities | Avg Entities/Record | PII Density |
|---|---|---|---|---|
| Train | 113,133 | 286,542 | 2.53 | 14.9% |
| Validation | 3,600 | 9,211 | 2.56 | 15.0% |
| Test | 3,600 | 9,271 | 2.58 | 15.2% |
Entity types (12): FULLNAME, UNKNOWN, PHONE, EMAIL, ADDRESS, DATE, FIRST_NAME, ID_NUMBER, ZIPCODE, CREDIT_CARD, SSN, ORGANIZATION
Test set is dominated by FULLNAME (57.5%) and UNKNOWN (19.6%).
Data Format
Each record is a JSON object:
{
"id": "sample_00123",
"original_text": "Please contact John Smith at john.smith@email.com for details.",
"anonymized_text": "Please contact Michael Jones at m.jones@mail.org for details.",
"entities": [
{"text": "John Smith", "type": "FULLNAME", "start": 15, "end": 25},
{"text": "john.smith@email.com", "type": "EMAIL", "start": 29, "end": 49}
]
}
Loading the Dataset
from datasets import load_dataset
dataset = load_dataset("huggingbahl21/saha-al")
# Access splits
train = dataset["train"] # 113,133 records
val = dataset["validation"] # 3,600 records
test = dataset["test"] # 3,600 records (frozen)
# Example record
record = test[0]
print(record["original_text"])
print(record["entities"])
Tasks
Task 1: PII Detection
Input: original_text | Output: detected_entities: [{start, end, type}]
Evaluate span-level precision, recall, and F1 in three modes: exact match, partial (IoU > 0.5), and type-aware.
Task 2: Text Anonymization
Input: original_text | Output: anonymized_text
Replace all PII with realistic synthetic alternatives while preserving non-PII text and document structure.
Task 3: Privacy Risk Assessment
Evaluator-computed over Task 2 outputs. Simulates adversarial attacks against anonymized text to measure residual re-identification risk.
Metrics
Anonymization Quality (Task 2)
| Metric | What it measures | Direction |
|---|---|---|
| ELR | Entity Leakage Rate — fraction of entities found verbatim in output | ↓ lower is better |
| Token Recall | Fraction of entity tokens absent from output | ↑ higher is better |
| OMR | Over-Masking Rate — non-entity tokens unnecessarily altered | ↓ lower is better |
| FPR | Format Preservation Rate — structured replacements match expected format | ↑ higher is better |
| BERTScore F1 | Semantic similarity between original and anonymized text | ↑ higher is better |
Privacy Risk (Task 3)
| Metric | Attack model | Direction |
|---|---|---|
| CRR-3 | Capitalized 3-gram survival rate (statistical) | ↓ lower is better |
| ERA@k | Entity Recovery Attack — retrieval adversary with candidate pool (SBERT) | ↓ lower is better |
| LRR | LLM Re-identification Rate — generative adversary guesses original entities | ↓ lower is better |
| UAC | Unique Attribute Combination rate — k-anonymity proxy | ↓ lower is better |
Privacy-Utility Tradeoff
PUS(lambda) = lambda * (1 - ELR/100) + (1 - lambda) * (BERTScore/100)
A single parameterized score where lambda controls the privacy-utility balance (lambda=0.5 gives equal weight).
Leaderboard
Evaluated on the frozen test split (3,600 records, 9,271 entities).
Task 2: Anonymization Quality
| System | ELR ↓ | Token Recall ↑ | BERTScore F1 ↑ | PUS (lambda=0.5) |
|---|---|---|---|---|
| BART-base + PII | 0.93% | 95.50% | 92.74 | 0.959 |
| Flan-T5-small + PII | 0.99% | 95.33% | 92.47 | 0.957 |
| T5-small + PII | 1.54% | 94.15% | 92.59 | 0.955 |
| T5-eff-tiny + PII | 4.14% | 90.42% | 92.57 | 0.942 |
| DistilBART + PII | 1.23% | 94.41% | 86.34 | 0.926 |
| spaCy+Faker | 26.44% | 74.59% | 91.86 | 0.827 |
| Presidio | 33.77% | 68.18% | 90.04 | 0.781 |
| Regex+Faker | 83.39% | 23.05% | 98.15 | 0.574 |
Task 3: Privacy Risk
| System | CRR-3 ↓ | ERA@1 ↓ | ERA@5 ↓ | LRR Exact ↓ | UAC ↓ |
|---|---|---|---|---|---|
| BART-base + PII | 34.62% | 1.90% | 4.84% | 0.13% | 0.33% |
| Flan-T5-small + PII | 34.94% | 1.67% | 5.08% | — | 0.22% |
| spaCy+Faker | 40.62% | 9.36% | 14.27% | — | 1.58% |
| Presidio | 50.33% | 20.46% | 26.09% | 2.12% | 1.78% |
Task 1: PII Detection
| System | Exact F1 | Partial F1 | Type-aware F1 |
|---|---|---|---|
| spaCy+Faker | 56.30 | 69.38 | 18.18 |
| Presidio | 48.57 | 60.11 | 4.97 |
| Regex+Faker | 25.86 | 27.99 | 24.39 |
Key Findings
- **Seq2seq models achieve <1% entity leakage** while preserving >92% semantic similarity, dramatically outperforming rule-based systems (26–83% leakage).
- Structured PII (email, phone, SSN, credit card) is fully solved — 0% leakage across all seq2seq models. Names remain the open challenge, accounting for 83% of residual leakage.
- Retrieval attacks are more effective than LLM attacks. ERA@1 recovers 1.9% of entities vs LRR's 0.13%, indicating replacement-based anonymization is more vulnerable to database-level adversaries than generative inference.
- BART-base dominates the Pareto frontier with PUS=0.959, achieving near-maximum privacy (99.1%) and high utility (92.7%).
Evaluation Quick Start
pip install faker spacy bert_score sentence-transformers transformers
python -m spacy download en_core_web_lg
Run a baseline
# Rule-based (regex / spacy / presidio)
python -m baselines.regex_faker_baseline --gold data/test.jsonl --mode spacy --save-spans
# Seq2seq (downloads checkpoint from HuggingFace)
python -m baselines.seq2seq_inference --gold data/test.jsonl --model-name bart-base-pii --output predictions/predictions_bart-base-pii.jsonl
Evaluate
# Task 2: Anonymization quality
python -m eval.eval_anonymization \
--gold data/test.jsonl \
--pred predictions/predictions_bart-base-pii.jsonl \
--print-types
# Task 1: Detection (requires --save-spans from baseline)
python -m eval.eval_detection \
--gold data/test.jsonl \
--pred predictions/spacy_spans.jsonl
# Task 3: Privacy risk
python -m eval.eval_privacy \
--gold data/test.jsonl \
--pred predictions/predictions_bart-base-pii.jsonl \
--train data/train.jsonl \
--skip-lrr
# Bootstrap confidence intervals
python -m eval.bootstrap \
--gold data/test.jsonl \
--pred predictions/predictions_bart-base-pii.jsonl \
--metrics elr token_recall crr3
# Failure taxonomy
python -m analysis.failure_taxonomy \
--gold data/test.jsonl \
--pred predictions/predictions_bart-base-pii.jsonl
# Pareto frontier analysis
python -m analysis.pareto_frontier \
--results Results/all_eval_results.json \
--plot figures/pareto_frontier.png
# Run the full pipeline
bash scripts/run_all.sh
Submission Format
Task 2 (one JSONL line per record):
{"id": "sample_00000", "anonymized_text": "Please contact Michael Jones at m.jones@mail.org."}
Task 1 (one JSONL line per record):
{"id": "sample_00000", "detected_entities": [{"start": 15, "end": 29, "type": "FULLNAME"}]}
Repository Structure
benchmark/
├── eval/ # Evaluation modules
│ ├── utils.py # Shared: span matching, format regexes, I/O
│ ├── eval_detection.py # Task 1: span P/R/F1
│ ├── eval_anonymization.py # Task 2: ELR, Token Recall, OMR, FPR, BERTScore
│ ├── eval_privacy.py # Task 3: CRR-3, ERA, LRR, UAC
│ └── bootstrap.py # 95% bootstrap confidence intervals
│
├── baselines/ # Baseline systems
│ ├── regex_faker_baseline.py # Regex / spaCy / Presidio pipelines
│ ├── seq2seq_inference.py # BART / T5 / Flan-T5 / DistilBART inference
│ ├── bert_ner_baseline.py # BERT-base token classifier (NER)
│ ├── llm_baseline.py # GPT-4o-mini / local LLM zero-shot
│ └── hybrid_baseline.py # spaCy detect + LLM replace
│
├── analysis/ # Analysis & visualization
│ ├── dataset_stats.py # Dataset statistics
│ ├── pareto_frontier.py # Privacy-utility Pareto + PUS sweep
│ ├── failure_taxonomy.py # 5-category error classification
│ └── plot_results.py # Publication figures
│
├── scripts/ # Utilities
│ ├── prepare_dataset.py # Build train/val/test from raw data
│ ├── push_to_hf.py # Push to HuggingFace Hub
│ └── run_all.sh # Full evaluation pipeline
│
├── data/ # Dataset splits (JSONL)
├── predictions/ # Model outputs
├── Results/ # Evaluation JSONs
├── figures/ # Plots
│
├── RESULTS.md # Key results for reporting
├── RESULTS_FULL.md # Exhaustive results reference
├── METHODOLOGY.md # Full methodology & theory guide
└── RESULTS_INTERPRETATION.md # In-depth results discussion
Trained Model Checkpoints
All seq2seq checkpoints are hosted on HuggingFace:
Repository: [JALAPENO11/pii_identification_and_anonymisations](https://huggingface.co/JALAPENO11/pii_identification_and_anonymisations)
| Model | Base Architecture | Parameters | Checkpoint Path |
|---|---|---|---|
| BART-base + PII | facebook/bart-base |
139M | checkpoints_pii_aware_loss/bart-base/best_model.pt |
| Flan-T5-small + PII | google/flan-t5-small |
77M | checkpoints_pii_aware_loss/flan-t5-small/best_model.pt |
| T5-small + PII | t5-small |
60M | checkpoints_pii_aware_loss/t5-small/best_model.pt |
| DistilBART + PII | sshleifer/distilbart-cnn-6-6 |
230M | checkpoints_pii_aware_loss/distilbart/best_model.pt |
| T5-eff-tiny + PII | google/t5-efficient-tiny |
16M | checkpoints_pii_aware_loss/t5-efficient-tiny/best_model.pt |
Benchmark Protocol
- No test set contamination — do not train or tune on
test.jsonl - Use the provided evaluation scripts for fair comparison
- Report compute — GPU type, training time, inference time
- Disclose pre-training data for LLM-based systems
Citation
If you use SAHA-AL in your research, please cite:
@misc{saha-al-2026,
title={SAHA-AL: A Multi-Task Benchmark for PII Anonymization with Adversarial Privacy Evaluation},
author={Mr. A},
year={2026},
howpublished={\url{https://huggingface.co/datasets/huggingbahl21/saha-al}},
}
Limitations
- Synthetic data: Source texts are generated via Faker-based templates. Results may not fully generalize to real-world PII distributions.
- English only: All data, models, and evaluation metrics are English-specific.
- Entity type ambiguity: ~19.6% of test entities are typed as UNKNOWN due to heuristic type inference limitations.
- Train/test entity overlap: 43.9% of test entity strings appear in training data, which may inflate seq2seq model performance.
- IAA: Inter-annotator agreement was entity-level F1=0.83.
License
This benchmark is released under the MIT License.