Authors:
(1) Anthi Papadopoulou, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway and Corresponding author (anthip@ifi.uio.no);
(2) Pierre Lison, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway;
(3) Mark Anderson, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway;
(4) Lilja Øvrelid, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway;
(5) Ildiko Pilan, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway. Authors: Authors: (1) Anthi Papadopoulou, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway and Corresponding author (anthip@ifi.uio.no); (2) Pierre Lison, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway; (3) Mark Anderson, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway; (4) Lilja Øvrelid, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway; (5) Ildiko Pilan, Language Technology Group, University of Oslo, Gaustadalleen 23B, 0373 Oslo, Norway. TTable of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Definitions 2.1 Definitions 2.2 NLP Approaches 2.2 NLP Approaches 2.3 Privacy-Preserving Data Publishing 2.3 Privacy-Preserving Data Publishing 2.4 Differential Privacy 2.4 Differential Privacy 3 Datasets and 3.1 Text Anonymization Benchmark (TAB) 3 Datasets and 3.1 Text Anonymization Benchmark (TAB) 3.2 Wikipedia Biographies 3.2 Wikipedia Biographies 4 Privacy-oriented Entity Recognizer 4 Privacy-oriented Entity Recognizer 4.1 Wikidata Properties 4.1 Wikidata Properties 4.2 Silver Corpus and Model Fine-tuning 4.2 Silver Corpus and Model Fine-tuning 4.3 Evaluation 4.3 Evaluation 4.4 Label Disagreement 4.4 Label Disagreement 4.5 MISC Semantic Type 4.5 MISC Semantic Type 5 Privacy Risk Indicators 5 Privacy Risk Indicators 5.1 LLM Probabilities 5.1 LLM Probabilities 5.2 Span Classification 5.2 Span Classification 5.3 Perturbations 5.3 Perturbations 5.4 Sequence Labelling and 5.5 Web Search 5.4 Sequence Labelling and 5.5 Web Search 6 Analysis of Privacy Risk Indicators and 6.1 Evaluation Metrics 6 Analysis of Privacy Risk Indicators and 6.1 Evaluation Metrics 6.2 Experimental Results and 6.3 Discussion 6.2 Experimental Results and 6.3 Discussion 6.4 Combination of Risk Indicators 6.4 Combination of Risk Indicators 7 Conclusions and Future Work 7 Conclusions and Future Work Declarations Declarations References References Appendices Appendices A. Human properties from Wikidata A. Human properties from Wikidata B. Training parameters of entity recognizer B. Training parameters of entity recognizer C. Label Agreement C. Label Agreement D. LLM probabilities: base models D. LLM probabilities: base models E. Training size and performance E. Training size and performance F. Perturbation thresholds F. Perturbation thresholds 5.4 Sequence Labelling Yet another approach to indirectly assess the re-identification risk based on masking decisions from experts is to estimate a sequence labelling model. Compared to the previous methods, this method is the one that is most dependent on the availability of in-domain, labeled training data. For this approach, we fine-tune a encoder-type language model on a token classification objective, each token being assigned to either MASK or NO MASK. For the Wikipedia biographies, we rely on a RoBERTa model (Liu et al., 2019), while we switch to a Longformer model (Beltagy et al., 2020) for TAB given the length of the court cases, as proposed in Pil´an et al. (2022). Due to discrepancies between the manually labeled spans or detected by the privacy-oriented entity recognizer, and the ones created by the fine-tuned model, we operate under two possible setups: • Full match: We assume that a span constitutes a high re-identification risk if all of its tokens are marked as MASK by the fine-tuned Longformer/RoBERTa. • Partial match: We consider that the span has a high risk if at least one token is marked as MASK by the Longformer/RoBERTa model. 5.5 Web Search We used the Google API to query for each target individual in a given document and the unique text spans that occur in a given document[7]. The Google API provides 10 results per page. We limit the experiment to the top 20 results (i.e. first two pages from the web search). To avoid a prohibitively high number of API calls, we also constrain the search to individual text spans, although the same approach can in principle be extended to combinations of PII spans. We also used the total number of hits reported by the Google search API for each PII span query. The assumption here is that if a search yields a larger number of responses, there is a higher chance that one of those responses will contain information about the target individual. However, generic search queries are also likely to return many responses. Therefore we considered applying an upper and lower bound on the total number hits. These thresholds were set experimentally to maximize the tokenlevel F1 scores on the TAB development set. This resulted in a lower limit of 100 hits and no upper limit. This method is limited by the potential unreliable nature of the total responses reported by web search engines, as shown in S´anchez et al. (2018). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [7] Web searches are from the period spanning July 2023 to September 2023.

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Improving Privacy Risk Detection with Sequence Labelling and Web Search

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