New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms.

Area of Science:

• Cloud Computing
• Data Security
• Information Retrieval

Background:

• Secure data searching in cloud platforms is challenging.
• Existing methods lack accuracy and adaptability for encrypted sensor data.
• Privacy concerns limit the utility of traditional search techniques.

Purpose of the Study:

• To propose a novel multi-keyword ciphertext search method (MCS-IQHC).
• To enhance search accuracy and self-adaptability in privacy-protected sensor cloud networks.
• To provide an efficient and secure search solution for encrypted data.

Main Methods:

• Document vectors generated using TF-IDF and VSM.
• Improved Quality Hierarchical Clustering (IQHC) for vector, document, and cluster indexing.
• Encryption of indices using the k-nearest neighbor (KNN) algorithm.
• Multi-keyword ciphertext search (MCS-IQHC) returning top-k results.

Main Results:

• MCS-IQHC demonstrates improved search accuracy.
• The method exhibits enhanced searching efficiency.
• Experimental results validate superior performance in high-privacy sensor cloud environments.
• The technique shows self-adaptability for multi-keyword searches.

Conclusions:

• MCS-IQHC offers a robust solution for secure multi-keyword searching in cloud environments.
• The proposed method outperforms existing state-of-the-art techniques in privacy-sensitive applications.
• This approach effectively balances search performance and data confidentiality.