Universal Keyword Classifier on Public Key Based Encrypted Multikeyword Fuzzy Search in Public Cloud.

Area of Science:

• Computer Science
• Information Technology
• Data Security

Background:

• Cloud computing offers cost benefits and flexibility but lacks data visibility for users.
• Effective data utilization in the cloud is challenged by security concerns and inefficient access methods.
• Fuzzy searching techniques are crucial for improving data access in public cloud storage.

Purpose of the Study:

• To reduce search time on cloud storage servers for enhanced data utilization.
• To address challenges in effective data access through advanced fuzzy searching techniques.
• To propose an efficient method for multikeyword fuzzy search in cloud environments.

Main Methods:

• Discussion of existing fuzzy searching techniques.
• Introduction of the Asymmetric Classifier Multikeyword Fuzzy Search method.
• Utilization of a BTree fuzzy searchable index to resolve inconsistencies and improve searchability.

Main Results:

• The proposed method significantly reduces search time for multiple keywords.
• A universal keyword classifier is created for multikeyword requests, optimizing search paths.
• The BTree index effectively handles typos and representation inconsistencies.

Conclusions:

• The Asymmetric Classifier Multikeyword Fuzzy Search method enhances cloud data utilization by reducing search latency.
• The approach effectively manages fuzzy keyword sets and improves data accessibility.
• This research contributes to more efficient and secure cloud data management strategies.