Research Center for Digital Sustainability

Re-Identification in Court Decisions with Pretrained Language Models

Re-Identification in Court Decisions with Pretrained Language Models

This project is available as a Bachelor's or Master's project.


Swiss court decisions are anonymized to protect the privacy of the involved people (parties, victims, etc.). Previous research [1] has shown that it is possible to re-identify companies involved in court decisions by linking the rulings with external data in certain cases. Our project tries to further build an automated system for re-identifying involved people from court rulings. This system can then be used as a test for the anonymization practice of Swiss courts. For more information regarding the overarching research project please go here.

We propose to approach the general problem of re-identification (not only for a specific domain) with pretrained language models such as BERT or T5. Roberts et al. [2] have shown that T5 can be used to answer questions in a closed book way (without context, solely drawing from the parameters!). We plan to pretrain a large language model on a corpus of documents related to Swiss court decisions (e.g. newspapers) and then fine-tune it on a small dataset of re-identified court decisions. Then we will run it on not re-identified court decisions and analyze the results.

Research Questions

So far, to the best of our knowledge, no one has tried re-identifying individuals occurring in court decisions with pretrained language models.

RQ1: How many individuals can be re-identified using pretrained language models (recall)?

RQ2: What percentage of re-identifications are actually correct (precision)? 


  1. Further pretrain a large language model on external data like newspaper articles (data is already available)
  2. Create a small dataset of re-identified court decisions (data is already available).
  3. Finetune the large language model on this dataset. There are different possibilities of framing the task:
    1. Feed the anonymized court decision to the language model and let it predict the anonymized entity via masked language modeling (like gap filling from primary school) [3]
    2. Rephrase the sentence with the anonymization to a question and feed the question to the language model.
    3. If necessary, try other approaches of using the language model for re-identification
  4. Detect and verify the re-identifications
  5. Analyze the experimental results


⬤⬤⬤◯◯ Programming

⬤⬤⬤⬤◯ Experimentation

⬤◯◯◯◯ Literature


Good programming skills (preferably in Python)

Preferably experience in deep learning (transformers)


Joel Niklaus


[1] Vokinger, K.N., Mühlematter, U.J., 2019. Re-Identifikation von Gerichtsurteilen durch «Linkage» von Daten(banken). Jusletter 27.
[2] Roberts, A., Raffel, C., & Shazeer, N.M. (2020). How Much Knowledge Can You Pack into the Parameters of a Language Model? ArXiv, abs/2002.08910.
[3] Kassner, N., Dufter, P., & Schütze, H. (2021). Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models. ArXiv, abs/2102.00894.