<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing <italic>τ</italic> when <italic>q</italic> = 0 The larger <italic>q</italic> is, the better the protection performance of the model is, and the optimal <italic>τ</italic> value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.</p>
</abstract>

In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk assessment scheme is designed by attribute sensitivity calculation, attribute similarity calculation, and attribute association calculation. Then a data privacy protection algorithm based on differential privacy is proposed, and the differential privacy data protection algorithm and implementation mechanism are described. Finally, the performance of the differential privacy protection algorithm is evaluated by analyzing the learning performance and protection performance of the algorithm. The results show that the learning performance of the differential privacy protection model decreases with increasing τ when q = 0 The larger q is, the better the protection performance of the model is, and the optimal τ value also shows a trend to the right. This study provides an effective method for user data privacy protection under the metaverse and offers new ideas for research in related fields.

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the metaverse, user data privacy protection has become an important issue. In this paper, firstly, a user data privacy leakage risk...

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Publié en ligne: 15 oct. 2023

Pages: -

Reçu: 16 janv. 2023

Accepté: 25 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00636

Mots clés
Metaverse, User data, Leakage risk assessment, Differential privacy, Privacy-preserving algorithms

© 2023 Lusheng Shi, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A study of user data privacy protection algorithms in the context of metaverse based on emotional AI IoT

Publié en ligne: 15 oct. 2023

Pages: -

Reçu: 16 janv. 2023

Accepté: 25 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00636

Mots clésMetaverse, User data, Leakage risk assessment, Differential privacy, Privacy-preserving algorithms

© 2023 Lusheng Shi, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Metaverse, User data, Leakage risk assessment, Differential privacy, Privacy-preserving algorithms