Improving the detection of the risk of privacy with sequence signs and searching on the web

Authors:
(1) Anthi Papadopoulou, Language Technology Group, Oslo University, Gaustadalleen 23B, 0373 Oslo, Norway and the corresponding author ([email protected]);
(2) Pierre Leson, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway;
(3) Mark Anderson, Norwegian Computing Center, Gaustadalleen 23A, 0373 Oslo, Norway;
4)
(5) Eldiko Pilan, Language Technology Group, Oslo University, Gaustadalleen 23B, 0373 Oslo, Norway.
Ttable from the links
Abstract and 1 introduction
2 background
2.1 Definitions
2.2 NLP approach
2.3 Publish privacy retaining data
2.4 Conversion Privacy
3 data sets and 3.1 standards of non -identification of text (tab)
3.2 CVs Wikipedia
4 The entity’s identifier for privacy
4.1 The characteristics of Wikidata
4.2 Silver Corpus and Model Tuning
4.3 Evaluation
4.4 Unlike the poster
4.5 Identity of the semantic type
5 indicators of privacy risk
5.1 LLM Possibilities
5.2 Extension classification
5.3 Disorders
5.4 Signs Sequence and 5.5 Search on the Internet
6 Analysis of the indicators of privacy and 6.1 evaluation measures
6.2 Experimental results and 6.3 discussions
6.4 sets of risk indicators
7 conclusions and future work
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Reference
Pursuit
A. Human characteristics from Wikidata
for. Entity recognition training parameters
C. The name agreement
Llm: basic models
E. Training and performance volume
Wow thresholds of turmoil
5.4 Signs
Another approach to assessing the risk of indirect re -identification based on the decisions of concealment from experts is the estimate of the series of signs. Compared to the previous methods, this method is the most dependent method of the availability of training data in the internal field.
For this approach, we set the encryption type language model on the goal of classifying the distinctive symbol, each symbol is set either for a mask or without a mask. For Wikipedia cars, we rely on the Roberta model (Liu et al (2022). Due to the contradictions between the hand -called handcrafted or discovered by the identification of the entity directed to privacy, and those created by the form that was seized, we work according to potential preparations:
• Full match: We assume that the period poses a high risk of restoring identity if all the distinctive symbols are marked as a mask by Longformer/Roberta.
• Partial match: We consider that it has high risks if at least one symbol is distinguished as the Longformer/Roberta mask.
5.5 Search on the Internet
We used Google API to inquire about each individual goal in a specific document and the extension of the unique text that occurs in a specific document[7]. Google API 10 provides results for each page. We limit the experience to the best 20 results (i.e. the first two pages of web search). To avoid a large number of API calls, we also lead to research restrictions on individual text periods, although the same approach can extend in principle to groups of PII periods.
We also used the total number of visits by the Google Search applications for each PII SPAN. The assumption here is that if the research gives more responses, there is a higher opportunity for one of these responses to contain information about the target individual. However, it is also possible that public search inquiries will return many responses. So we thought about applying the upper and lower limit to the total number. These thresholds are experimentally assigned to increase the F1 degrees in the Tokenlevel Group on the tabs brand development set. This led to a less than 100 visits, not the upper limit. This method is limited to the potential, unreliable nature of the total responses that search engines on the web, as shown in S´anchez et al. (2018).
[7] Web searches from the period that extends to July 2023 to September 2023.