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International Conference on Security and Privacy in New Computing Environments

SPNCE 2019: Security and Privacy in New Computing Environments pp 152–177Cite as

Privacy Disclosures Detection in Natural-Language Text Through Linguistically-Motivated Artificial Neural Networks

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Abstract

An increasing number of people are sharing information through text messages, emails, and social media without proper privacy checks. In many situations, this could lead to serious privacy threats. This paper presents a methodology for providing extra safety precautions without being intrusive to users. We have developed and evaluated a model to help users take control of their shared information by automatically identifying text (i.e., a sentence or a transcribed utterance) that might contain personal or private disclosures. We apply off-the-shelf natural language processing tools to derive linguistic features such as part-of-speech, syntactic dependencies, and entity relations. From these features, we model and train a multichannel convolutional neural network as a classifier to identify short texts that have personal, private disclosures. We show how our model can notify users if a piece of text discloses personal or private information, and evaluate our approach in a binary classification task with 93% accuracy on our own labeled dataset, and 86% on a dataset of ground truth. Unlike document classification tasks in the area of natural language processing, our framework is developed keeping the sentence level context into consideration.

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Notes

  1. 1.

    It is worth mentioning that we get little fluctuation on the accuracy value while changing the number of neurons in these layers. It seems obvious because, this layer might have needed more neurons for better non-linearity understanding when it sees relatively more data.

  2. 2.

    https://anonymous.4open.science/repository/3c84ab7b-02ce-4fd7-b982-f278d6f3c4f4/.

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Acknowledgments

The authors would like to thank National Science Foundation for its support through the Computer and Information Science and Engineering (CISE) program and Research Initiation Initiative(CRII) grant number 1657774 of the Secure and Trustworthy Cyberspace (SaTC) program: A System for Privacy Management in Ubiquitous Environments.

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Authors and Affiliations

  1. Boise State University, Boise, ID, 83702, USA

    Nuhil Mehdy, Casey Kennington & Hoda Mehrpouyan

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  1. Nuhil Mehdy
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  3. Hoda Mehrpouyan
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Correspondence to Hoda Mehrpouyan .

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Editors and Affiliations

  1. Guangzhou University, Guangdong, Guangdong, China

    Jin Li

  2. College of Cyberspace Security, Nankai University, Tianjin, Tianjin, China

    Zheli Liu

  3. College of Mathematics and Computer Science, Zhejiang Normal University, Zhejiang, Zhejiang, China

    Hao Peng

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Mehdy, N., Kennington, C., Mehrpouyan, H. (2019). Privacy Disclosures Detection in Natural-Language Text Through Linguistically-Motivated Artificial Neural Networks. In: Li, J., Liu, Z., Peng, H. (eds) Security and Privacy in New Computing Environments. SPNCE 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-21373-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-21373-2_14

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