MultiGraSCCo: A Multilingual Anonymization Benchmark with Annotations of Personal Identifiers
arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information are crucial for developing and testing anonymization systems to enable safe data sharing that complies with privacy regulations. Since accessing real patient data is a bottleneck, synthetic data offers an efficient solution for data scarcity, bypassing privacy regulations that apply to real data. Moreover, neural machine translation can help to create high-quality data for low-resource languages by translating validated real or synthetic data from a high-resource language. In this work, we create a multilingual anonymization benchmark in ten languages, using a machine translation methodology that preserves the original annotations and renders names of cities and people in a culturally and contextually appropriate form in each target language. Our evaluation study with medical profe
arXiv:2603.08879v1 Announce Type: new Abstract: Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information are crucial for developing and testing anonymization systems to enable safe data sharing that complies with privacy regulations. Since accessing real patient data is a bottleneck, synthetic data offers an efficient solution for data scarcity, bypassing privacy regulations that apply to real data. Moreover, neural machine translation can help to create high-quality data for low-resource languages by translating validated real or synthetic data from a high-resource language. In this work, we create a multilingual anonymization benchmark in ten languages, using a machine translation methodology that preserves the original annotations and renders names of cities and people in a culturally and contextually appropriate form in each target language. Our evaluation study with medical professionals confirms the quality of the translations, both in general and with respect to the translation and adaptation of personal information. Our benchmark with over 2,500 annotations of personal information can be used in many applications, including training annotators, validating annotations across institutions without legal complications, and helping improve the performance of automatic personal information detection. We make our benchmark and annotation guidelines available for further research.
Executive Summary
This article introduces MultiGraSCCo, a multilingual anonymization benchmark designed to address the challenges of accessing sensitive patient data for machine learning purposes. The benchmark features annotations of personally identifiable information in ten languages, leveraging machine translation to preserve the original context and cultural nuances. The evaluation study with medical professionals confirms the quality of the translations, showcasing the potential of this benchmark in various applications, including training annotators, validating annotations, and improving automatic personal information detection. The authors make their benchmark and guidelines available for further research, contributing significantly to the development of anonymization systems and safe data sharing practices.
Key Points
- ▸ MultiGraSCCo is a multilingual anonymization benchmark with annotations of personally identifiable information in ten languages.
- ▸ Machine translation methodology preserves the original context and cultural nuances of the source language.
- ▸ Evaluation study with medical professionals confirms the quality of the translations and their adaptability to diverse cultural contexts.
Merits
Comprehensive Coverage of Personal Identifiers
The benchmark offers a wide range of annotations for personal information, making it a valuable resource for researchers and developers.
Culturally Sensitive Translations
The machine translation methodology ensures that names of cities and people are translated in a culturally and contextually appropriate form.
Scalability and Flexibility
The benchmark can be used in various applications, including training annotators, validating annotations, and improving automatic personal information detection.
Demerits
Limited Scope to Real-World Applications
While the benchmark is comprehensive, its applicability to real-world scenarios may be limited due to the synthetic nature of the data.
Lack of Evaluation Metrics for Anonymization Systems
The article does not provide a clear evaluation framework for assessing the performance of anonymization systems using the benchmark.
Expert Commentary
The introduction of MultiGraSCCo represents a significant contribution to the field of anonymization and machine learning. By providing a comprehensive benchmark with culturally sensitive translations, the authors have taken a crucial step towards addressing the challenges of accessing sensitive patient data. However, it is essential to consider the limitations of synthetic data and the need for more robust evaluation metrics for anonymization systems. Furthermore, the development of anonymization benchmarks like MultiGraSCCo has significant practical and policy implications, including the facilitation of safe data sharing and the development of more effective privacy regulations.
Recommendations
- ✓ Future research should focus on evaluating the performance of anonymization systems using the benchmark and developing more robust evaluation metrics.
- ✓ The development of anonymization benchmarks like MultiGraSCCo should be extended to include more languages and cultural contexts to ensure broader applicability.