Details

Autor(en) / Beteiligte

• Satyanarayana, Ch
• Gao, Xiao-Zhi
• Ting, Choo-Yee
• Muppalaneni, Naresh Babu

Titel

Black Hole Algorithm for BigData Anonymization

Ist Teil von

• Proceedings of the International Conference on Computer Vision, High Performance Computing, Smart Devices and Networks, 2022, p.29-40

Ort / Verlag

Singapore: Springer

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Due to advancements in technologies, data is increasing tremendously per day and preserving privacy for such datasets is complex. K-anonymity is the well Algorithm to preserve privacy which supports generalization. Although the scope of privacy protection is solved by generalization-based algorithm but fails to solve the information loss issue. Later on, a clustering-based approach was developed to enhance the information quality which also fails to find the optimal solution. Hence a unique approach of Black Hole Algorithm for Big Data to achieve anonymization to support all these limitations is proposed. Experiments results illustrate that data utility has been enhanced related to standard k-anonymity centered MapReduce besides Clustering-centered MapReduce approach.