5 ways to measure the risk of privacy in text data

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

5 indicators of privacy risk

Many methods of cleansing the text simply work by hiding all the discovered PII stretching. However, this may lead to excessive excess, as the actual risks of re -identifying the identity may differ greatly from time to time. In many documents, a large part of the discovered text periods may be kept in a clear text without increasing the risk of re -identifying Hayid. For example, in Tab Corpus, only 4.4 % of entities were distinguished by identifiers as direct identifiers, 64.4 % of illusion, leaving 31.2 % of the entities in a clear text. To evaluate any stretch text must be masked, we need to design the indicators of the risk of privacy capable of identifying the periods of the text (or a combination of text periods) that already constitute the risk of restoring identity.

We offer 5 possible methods to infer the risk of re -identity associated with the text extensions in a document. These five indicators depend on:

1. LLM’s possibilities,

2. Extension classification,

3. Disorders,

4. Serial mode,

5. Search on the Internet.

The research approach can be applied to the web in a zero way without any technician. The two ways, respectively, require LLM’s possibilities and disorders, a small number of examples called the classification threshold or suitable for a simple binary classification model. Finally, the SPAN classification methods and sequence sequence are by formulating an existing language model, and therefore they are the most reliable ways to obtain a sufficient amount of training data called to reach the peak performance. Training data usually take the form of human decisions to hide or maintain a clear text on a specific text.

We present each method in turn and provide an evaluation and discussion of its benefits and relative restrictions in Section 6.

5.1 LLM Possibilities

The possibility of an extension as expected by the language model is reversed with informatics: The period that is difficult to predict is the most beneficial/surprising that the language model can easily conclude from the context (Zarcone et al., 2016). The basic intuition is that the periods of texts that are very useful/sudden are also associated with the risks of high identity, because they often correspond to specific names, dates or symbols that cannot be predicted from context.

Tangantly, we calculate the possibility of each PII period that was discovered in view of the full context of the text by hiding all (sub -) words of the substitute, and returning a list with all record operations (one for each symbol) indicating the calculated range through a large dual -direction language model, in our BERT (large, large) (large) et al., 2019). Then these possibilities are assembled and employed as features of a binary workbook that may emerge the possibility that the extension of the text will be hidden by a human broadcaster.

The list of registry operations for each period has a different period depending on the number of distinctive symbols in the text period. So we assemble this list by reducing it to 5 features, which are minimumand maximumand middle and Intended The probability of the record as well as total From registry operations in the list. In addition, we also include an attribute of the PII type designated type of traffic by human broadcasters (see Table 1).

For the work itself, we perform experiments using a simple logistical slope model as well as a more advanced car classification framework. Automl (He et al We use the Autogluon (Ericson et al After training each model and the teacher’s teacher improves each of them, a group is likely to be trained using a stacking technique. In Table 11, menu appears in all basic models for the predicament, in the D. We use training divisions for Wikipedia and Tab Corpus to suit the workbook.