Ethics and Privacy in Artificial Intelligence: Exploring the Intersection of Technology and Morality

DOI

10.2991/978-94-6239-733-0_39How to use a DOI?

Keywords

Data Privacy; Anonymization; Pseudonymization; Hashing; Re-identification Risk; AI Data Protection

Abstract

The increasing use of data in artificial intelligence (AI) systems has raised significant concerns about data privacy. This study explores a practical approach to privacy protection using lightweight anonymization techniques. Specifically, hash functions and pseudonymization are applied to sensitive attributes in an online shopping dataset to reduce identifiability. The effectiveness of the approach is evaluated by analyzing re-identification risk before and after anonymization. The results show that the proposed method reduces privacy risks while preserving data usability for analysis. This study highlights the trade-off between simplicity and effectiveness in real-world data privacy protection and provides a practical perspective for applying anonymization methods in AI-related applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xianghong Jin
PY  - 2026
DA  - 2026/07/17
TI  - Ethics and Privacy in Artificial Intelligence: Exploring the Intersection of Technology and Morality
BT  - Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)
PB  - Atlantis Press
SP  - 377
EP  - 384
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-733-0_39
DO  - 10.2991/978-94-6239-733-0_39
ID  - Jin2026
ER  -