![]() The target of a document may contain complex information. In addition, the author’s name as privacy information in the text also belongs to one of a feature in the document. In detail, the sensitive subject \(S_i\) as privacy information is the subject’s identity from a text piece. In addition, if the dimensionality of vector documents is reduced, extracting or training work may expose sensitive information, even if the classification result for documents or sentences in the documents can be protected by utilizing differential privacy . However, the normal documents are complicated and heterogeneous, which are tough to be expressed through those thousands of words. Considering this limitation, only thousands of words can be selected in natural language processing. Although document vectors’ dimensionality can be reduced by restricting the number of feature words, if there are synonyms in the contexts, the model cannot reduce the dimensionality by filtering feature words . However, Naive Bayes (NB) and k-nearest neighbors require the input texts to have relatively low dimensions (less than 10). It uses the storage form of real-valued vectors to make the distribution of some words in vector spaces with similar purposes similar. ![]() In the natural language process(NLP), Word embedding is a method of encoding the meaning of words. Using the word embedding model, naive Bayes, k-nearest neighbors, and support vector machine are three typical methods for text classification. Text classification using the Word embedding model is an important step in extracting the information from documents. ![]() In 2019, Miles proposed a membership attack methods to attack the personal data-sets via the weakness of word embedding in text classification task . However, privacy leakage will happen when using word embedding models. The word vector generated by the word vector model is input into the classification model for classification tasks, and its accuracy is higher . The mapping process from observation to vector can process by probability model, neural networks, dimension reduction. Word embedding is a language modeling in natural language processing (NLP) in this type, the word and phrases will reflect in the vectors. In addition, the fusion study and privacy threat evaluation also verify that the proposed PPDIFSEA method combined with WECPPSVM achieves an acceptable level of classification accuracy and privacy protection. Our quantitative analysis shows that the WECPPSVM proposed in this paper can approach mainstream text classification algorithms in terms of text classification accuracy while preserving privacy without increasing computational complexity. And this model can protect privacy by injecting the privacy noise into the classification result, this method can interfere with the background knowledge-based privacy attack. PB is an indispensable condition for both data sampling and privacy noise generation. In addition, this paper also proposes the Privacy-preserving Distribution and Independent Frequent Subsequence Extraction Algorithm (PPDIFSEA), which calculates the degree of independence of the training data input to the classification model by training the Deep Belief Network(DBN) in PPDIFSEA, then obtains the Privacy Boundary(PB). In short, if you need a reliable, free and easy-to-use PDF compressor, give PDF Compressor a try today.To effectively extract and classify the information from reports or documents and protect the privacy of the extracted results, we propose a privacy classification named Word Embedding Combination Privacy-preserving Support Vector Machine (WECPPSVM) model to classify the text. And, if your PDF documents are password protected, don't worry - PDF Compressor can handle password protection, too.įinally, PDF Compressor doesn't require Adobe Acrobat, which is a big bonus for those who don't want to pay for premium software or deal with overwhelming features they will never use. You can also import a text list of files to be shrunk. It can handle whole folders of PDF documents and even their subfolders without a problem. If you need to compress multiple files at once, PDF Compressor has you covered. Plus, if you are experienced with scripting, you can also use the tool's command line functionality for automation. This means that you can start compressing files with minimal knowledge or experience required. You can drag and drop PDF files into the program, or use its integrated Windows Explorer function. One of the standout features of PDF Compressor is its ease of use. Additionally, it is available in multiple languages, making it a versatile option no matter where you are. This free software allows you to easily compress PDF documents and significantly reduce their file size. If you're looking for an efficient PDF compressor, PDF Compressor might be the solution you're seeking.
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