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.
Links table
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
6.2 Experimental results
We first evaluated the five -year privacy indicators using PII stretching by human commentators from Corpus and Wikipedia CV. Results are shown in Table 5. LLM -based methods are trained and Span classification of the corresponding training group (either Wikipedia or TAB), and the threshold of the method -based method has been modified by the training group. The search results on the web depend on the two alternative technology provided in Section 5.5, respectively based on the URL or the estimated number of visits. We also offer the results obtained with a simple basic line that plays all PII stretching.
Then we evaluate risk indicators with PII periods that were actually discovered by identifying the entity directed to the described privacy in Section 4, allowing us to assess the comprehensive performance of the proposed approach. Results are displayed in Table 6. Note that, in this setting, the foundation line leads the majority base (which extends all PII’s extensions) to call less than 1 due to the detection errors arising from the entity’s identifier.
6.3 Discussion
We are now discussing the experimental results of each privacy risk indicator one by one.
Llm possibilities
We can note that the car model, which consists of a group of overseeing works such as decision -making with excessive vocabulary on the development group, outperforms the simplest logistical slope model. In other words, the task of predicting high -risk text extensions due to the combined possibilities obtained from a large language model requires non -linear decision limits.
We also notice a big difference between the CVs Wikipedia and Tab Corpus. A closer look at the symbolic registry processes made by the decision to hide the decision, as shown in Figure 3, is especially useful. While Pii extends from CVs Wikipedia shows a clear difference between the possibilities of the record and unconvincing symbols, this is not the case for Tab Corpus[8].
The workbook reaches its optimal performance relatively, after monitoring about 1 % of the total size of the training group in both Corpora. Steeling experiences also show that the most important feature of the work is the type PII, where some species are hidden by human communicators more than others.
Extension classification
The SPAN classification approach improves classification compared to the trained workbook only on the possibilities collected from LLM, especially for the non -disclosure criteria.
Since this training includes the formulation of a large linguistic model, it requires a little more training data than the exclusive workbook on the chances of LLM and PII types. We note experimentally that the performance of the workbook settles at about 10 % of the training counterparts of both data groups. Depending on the truck experiments, we also see that the modified language model outcomes play a major role in the final prediction, along with the type PII.
Disorders
Despite its theoretical benefits (such as the possibility of direct evaluation to the extent of PII SPAN’s contribution to predicting a direct personal identifier), the method based on the disorder performs a bad performance, and it does not seem to improve on the basis of the base base of the majority. In fact, the turmoil mechanism, in conjunction with the cost function used to fix the threshold on registry operations, hides the vast majority of the text.
Signs
In general, this approach appears to provide the best balance between the degrees of accuracy and summons, which is expected from a large linguistic model that has been seized on the manual data. In contrast to the SPAN classification method, which only looks at the symbols inside the Pii SPAN itself, the approach to placing signs can take into account the context surrounding every period.
One should notice that the results are obtained in the 5 and 6 Tables with a virtual threshold of 0.5 on the possibility of masking. Of course, this threshold can be adjusted to increase the relative importance of dozens of retrieval, as is the case often in clearing the text to increase the cost of wrong negatives.
The way it depends on partial matches, which is the risk -fraught PII period if at least one symbol is distinguished by the risks and provides the best results. We also note that the results on CVs Wikipedia are lower than those obtained on the tab. This is likely to be due to the lowest number of CVs available for training (453 short texts) compared to the 1014 documents in the training group on the tab.
Search on the Internet
The table shows 5 clear teams between the use of URL intersection and the use of the estimated number of visits, while showing the first decrease in the summons while the latter shows a more balanced performance between accuracy and summons. Nevertheless, URL intersection provides a better explanation of the number of visits, as one can direct the user to the actual URL that contributes to re -recognition.
While using the URL intersection by restricting the number of pages, the chances of a useful intersection between the target and entities in the text reduce the deepest in the web’s tail in which we delve deeper. The potential solution is to try to analyze the text in URL to assess whether the target person is mentioned or not.
On the other hand, the number of visits that reach some extent is a better approach to risky entities, although the number of visits is not reliable as shown by the application programming interface. Notice, though, the restrictions we have already mentioned in Section 5.5 which must affect performance. Although the web can be considered almost useful and detailed for the potential basic knowledge that the attacker can enjoy and use for re -identity purposes, the technical details of this use make it difficult to explain the reason behind the performance clearly.
summary
As expected, the indicators of the trained privacy risk provides manually called data, especially a sequence, the best performance when comparing the distances that were determined as high risk with expert explanations. However, the complex textual data with rescue decisions is rare to the lack of many areas of text cleansing.
When it comes to data interest, we also notice that the exact result is customized by information content (PW) higher than the normal degree of accuracy of all indicators of privacy risk. He also argued in Bilan and others. (2022), this weighted result is more beneficial than the basic degree of accuracy because it takes into account the informational of each symbol. As a result, the likely degree of likely likely means that excessive occurrence tends to occur to the less beneficial symbols.
6.4 sets of risk indicators
Finally, we also evaluate the performance of the risk indicators combined. More specifically, we consider the PII period, which has been manually discovered highly dangerous if it is distinguished in this way at least One, two or three risk indicators.[9] The resulting performance appears in Table 7.
Although combining the indicators of the risk of privacy with ≥ 3 positive signals lead to excessive excess, they discover all direct identifiers and all semi -identical performance while maintaining a higher accuracy than the major base line. This high summons is important in cleansing the text, because the cost of ignoring the high -risk PII period is much higher than the wrong positive cost. Although excessive reducing roles slightly reduces the benefit of data (by making the text less reading or stripping it of some useful content), a false negative presence means that it is still possible to redefine the individual (ovens), and therefore its specificity is not fully guaranteed.
[8] At least when assembling on all types of PII. However, if we do so, we put these registered capabilities according to the type PII, then we notice a difference in registry processes of a number of PII types.
