Data Aggregation and Privacy Preserving Using Computational Intelligence

Abstract

In today's smart world, the privacy protection of data is an important issue. Data is distributed, reproduced, and disclosed with extensive use of communication technologies. Many non-traditional challenges arise with the rapid increase of IoT devices for system design and implementation. However, security and privacy are the main issues in IoT. With advanced technologies, an illegal copy of the content can easily be generated and shared. Therefore, it is crucial for users to protect and secure their data. In the said perspective, an efficient third-generation watermarking technique is proposed, which works on the computational intelligence model to insert a large amount of robust watermark and make an extra effort to hide more information than first and second-generation techniques. The Advanced Encryption Standard (AES) encryption algorithm is employed to guarantee secure communication, which has a significantly less computational cost. The proposed technique evaluated parameters including security, robustness, imperceptibility, and capacity. The results of the proposed technique are compared to existing text watermarking methods, which illustrates it is secure, robust, imperceptible, and inserts a large amount of watermark information through computational intelligence.