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The effects of different personal data categories on information privacy concern and disclosure

Published: 01 November 2021 Publication History

Abstract

The potential threats of exposing personal data associated with online services have been a reason for concern, and individuals as customers may decline to disclose their data due to trust issues. Literature has shown evidence that greater transparency in the types and purposes of data requested encourages individuals to disclose personal data. This evidence indicates a need to examine the characteristics of personal information practices. Furthermore, current data privacy regulations recognize the presence of different data characteristics such as location-specific, health-specific, and financial-specific. Yet, current legislations are formed to identify personal data as a singular category regardless of the requirements, including the specification of processed personal data to be relevant and limited to what is necessary for enabling service functions. Without categorization, measuring “relevant” and “necessary” can be ambiguous. Several pieces of researches have explored the impact of personal information type and sensitivity level on privacy concern and disclosure; however, most of them lacked an in-depth examination of data categorization with systematic validation. Our study aims to fill this gap, and additionally further look into how contextual demographic factors influence the perception on information privacy concern and disclosure of different personal data categories from a Malaysian perspective. Our study provides new evidence of validated personal data categories and their significant differences in perceived information privacy concern and disclosure intention. Our research finding also discovers that Age, Gender, and Working Industry, as demographic factors, have significant effects on disclosure intention associated with Tracking, Finance, Authenticating, and Medical-health information.

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      Published In

      cover image Computers and Security
      Computers and Security  Volume 110, Issue C
      Nov 2021
      504 pages

      Publisher

      Elsevier Advanced Technology Publications

      United Kingdom

      Publication History

      Published: 01 November 2021

      Author Tags

      1. Personal data categorization
      2. Information disclosure
      3. Privacy concern
      4. Information privacy
      5. Privacy by design

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      • (2022)How to Understand Data Sensitivity? A Systematic Review by Comparing Four DomainsProceedings of the 4th International Conference on Big Data Engineering10.1145/3538950.3538953(13-20)Online publication date: 26-May-2022

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