A Step to Achieve Personalized Human Centric Privacy Policy Summary

被引:0
作者
Simon, Ivan [1 ]
Haggag, Sherif [1 ]
Haggag, Hussein [2 ]
机构
[1] Univ Adelaide, Fac Sci Engn & Technol, Adelaide, SA, Australia
[2] Umea Univ, Dept Comp Sci, Umea, Sweden
来源
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL APPROACHES TO SOFTWARE ENGINEERING, ENASE 2023 | 2023年
关键词
Privacy Policy; Text Summarization; Human-Centric; Machine Learning; IDENTITY THEFT VICTIMIZATION; CYBERCRIME VICTIMIZATION; ONLINE PRIVACY; SECURITY;
D O I
10.5220/0011842600003464
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Online users continuously come across privacy policies for the service they use. Due to the complexity and verbosity of policies, majority of the users skip the tedious task of reading the policy and accept it. Without reading and evaluating the document users risk giving up all kinds of rights to their personal data and for the most part, are unaware of the data sharing and handling process. Efforts have been made to address the complex and lengthy structure of privacy policies by creating a standardized machine-readable format of privacy policies for the web browsers to process it automatically, a repository of crowdsourced summarized versions of some privacy policies, or by using natural language processing to summarize the policies. PirvacyInterpreter is one unique tool that acknowledges human-centric factors while summarising the policy. Thus, it generates a personalised summary of the privacy policy for the user providing relevant information to appease their privacy concerns. This paper presents the conceptualization of PrivacyInterpreter and implements a proof-of-concept model using configured RoBERTa(base) model to classify a privacy policy and produce a summary based on privacy aspects that reflect users' privacy concerns.
引用
收藏
页码:381 / 395
页数:15
相关论文
共 52 条
  • [1] Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset
    Amos, Ryan
    Acar, Gunes
    Lucherini, Elena
    Kshirsagar, Mihir
    Narayanan, Arvind
    Mayer, Jonathan
    [J]. PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021), 2021, : 2165 - 2176
  • [2] Gender difference and employees' cybersecurity behaviors
    Anwar, Mohd
    He, Wu
    Ash, Ivan
    Yuan, Xiaohong
    Li, Ling
    Xu, Li
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2017, 69 : 437 - 443
  • [3] Mobile application security: Role of perceived privacy as the predictor of security perceptions
    Balapour, Ali
    Nikkhah, Hamid Reza
    Sabherwal, Rajiv
    [J]. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2020, 52
  • [4] International differences in information privacy concerns: A global survey of consumers
    Bellman, S
    Johnson, EJ
    Kobrin, SJ
    Lohse, GL
    [J]. INFORMATION SOCIETY, 2004, 20 (05) : 313 - 324
  • [5] Online privacy concerns: A broad approach to understanding the concerns of different groups for different uses
    Bergstrom, Annika
    [J]. COMPUTERS IN HUMAN BEHAVIOR, 2015, 53 : 419 - 426
  • [6] Bracamonte V., 2019, INT C INF SYST SEC P, P186
  • [7] The role of privacy policy on consumers' perceived privacy
    Chang, Younghoon
    Wong, Siew Fan
    Fernando Libaque-Saenz, Christian
    Lee, Hwansoo
    [J]. GOVERNMENT INFORMATION QUARTERLY, 2018, 35 (03) : 445 - 459
  • [8] Policy to Avoid a Privacy Disaster
    Culnan, Mary J.
    [J]. JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS, 2019, 20 (06): : 848 - 856
  • [9] Devlin J, 2019, Arxiv, DOI arXiv:1810.04805
  • [10] Got Phished? Internet Security and Human Vulnerability
    Goel, Sanjay
    Williams, Kevin
    Dincelli, Ersin
    [J]. JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS, 2017, 18 (01): : 22 - 44