An innovative privacy preserving technique for incremental datasets on cloud computing

Area of Science:

• Computer Science
• Information Security
• Data Privacy

Background:

• Cloud computing (CC) offers significant processing power and storage, enabling easy data sharing and application deployment.
• However, data sharing in CC environments increases vulnerability to phishing attacks and malware, compromising data privacy.
• Sensitive applications, such as health services on the cloud, require robust security measures due to economic and operational benefits.

Purpose of the Study:

• To propose a new anonymization technique for cloud computing environments.
• To enhance data privacy and utility for distributed and incremental datasets.
• To address the limitations of existing anonymization methods in CC.

Main Methods:

• Development of a novel anonymization technique tailored for distributed and incremental datasets in CC.
• Implementation of the technique to re-anonymize datasets, focusing on privacy preservation and data utility.
• Performance evaluation to demonstrate the effectiveness of the proposed method.

Main Results:

• The proposed anonymization technique achieves better privacy protection for distributed and incremental datasets on CC.
• High data utility is maintained alongside enhanced confidentiality requirements.
• The technique demonstrates proficiency in data privacy preservation.

Conclusions:

• The developed anonymization technique offers improved security and privacy for cloud computing data.
• It effectively mitigates risks associated with data sharing in CC, especially for sensitive applications.
• The method provides a viable solution for privacy challenges in distributed and incremental cloud datasets.