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AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing

Author

Listed:
  • Francesco Contu

    (Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy)

  • Andrea Demontis

    (Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy)

  • Stefano Dessì

    (Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy)

  • Marco Muscas

    (Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy)

  • Daniele Riboni

    (Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy)

Abstract

Thanks to the popularity of personal mobile devices, more and more of the different types of private content, such as images and videos, are shared on social networking applications. While content sharing may be an effective practice to enhance social relationships, it is also a source of relevant privacy issues. Unfortunately, users find it difficult to understanding the terms and implications of the privacy policies of apps and services. Moreover, taking privacy decisions about content sharing on social networks is cumbersome and prone to errors that could determine privacy leaks. In this paper, we propose two techniques aimed at supporting the user in taking privacy choices about sharing personal content online. Our techniques are based on machine learning and natural language processing to analyze privacy policies, and on computer vision to assist the user in the privacy-conscious sharing of multimedia content. Experiments with real-world data show the potential of our solutions. We also present ongoing work on a system prototype and chatbot for natural language user assistance.

Suggested Citation

  • Francesco Contu & Andrea Demontis & Stefano Dessì & Marco Muscas & Daniele Riboni, 2021. "AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing," Future Internet, MDPI, vol. 13(6), pages 1-21, May.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:139-:d:559886
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    References listed on IDEAS

    as
    1. Marco Spruit & Drilon Ferati, 2020. "Text Mining Business Policy Documents: Applied Data Science in Finance," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 11(2), pages 28-46, July.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. repec:igg:jbir00:v:11:y:2020:i:2:p:1-19 is not listed on IDEAS
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    Cited by:

    1. Roberto Saia & Salvatore Carta & Olaf Bergmann, 2021. "Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures," Future Internet, MDPI, vol. 13(9), pages 1-3, September.
    2. Bhavani Malisetty & Alfredo J. Perez, 2024. "Evaluating Quantized Llama 2 Models for IoT Privacy Policy Language Generation," Future Internet, MDPI, vol. 16(7), pages 1-17, June.

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