Segyeong Bang^#, Soeun Kim^#, Gaeun Ahn, Hyemin Hong, Junhyoung Oh^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4629-4643, 2025, DOI:10.32604/cmc.2025.068221 - 23 October 2025

Abstract This study analyzes the risks of re-identification in Korean text data and proposes a secure, ethical approach to data anonymization. Following the ‘Lee Luda’ AI chatbot incident, concerns over data privacy have increased. The Personal Information Protection Commission of Korea conducted inspections of AI services, uncovering 850 cases of personal information in user input datasets, highlighting the need for pseudonymization standards. While current anonymization techniques remove personal data like names, phone numbers, and addresses, linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data. To address this,… More >

Tatsuhito Hasegawa^#,*, Kyosuke Fujino^#

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2429-2454, 2025, DOI:10.32604/cmc.2025.066270 - 03 July 2025

Abstract Recent advances in wearable devices have enabled large-scale collection of sensor data across healthcare, sports, and other domains but this has also raised critical privacy concerns, especially under tightening regulations such as the General Data Protection Regulation (GDPR), which explicitly restrict the processing of data that can re-identify individuals. Although existing anonymization approaches such as the Anonymizing AutoEncoder (AAE) can reduce the risk of re-identification, they often introduce substantial waveform distortions and fail to preserve information beyond a single classification task (e.g., human activity recognition). This study proposes a novel sensor data anonymization method based… More >

Burak Cem Kara^1,2,*, Can Eyüpoğlu¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1515-1535, 2023, DOI:10.32604/cmc.2023.040274 - 30 August 2023

Abstract Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, k-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a… More >