This study introduces a fresh approach to boost the security and accuracy of predicting photovoltaic (PV) power generation using secure aggregation techniques. The author will focus on several key stages in the PV data lifecycle, such as collecting, transmitting, storing, and analyzing data. To protect against potential attacks and prevent information leaks during these four crucial processes, we use Paillier and BVG homomorphic encryption methods. By combining TLS protocol with edge computing during data transmission, we not only improve data security but also reduce latency while tackling threats from attackers. There are two different types of attackers: honest but curious adversaries and active adversaries. Additionally, we make strategies for key management, access control, and auditing to ensure that access to information is monitored to help enhance overall system security. In the final phase of analyzing and predicting PV power output, we utilize advanced models like LSTM networks and CEEMDAN to achieve precise time-series predictions. The results indicate that these methods can effectively manage large PV datasets while keeping high prediction accuracy alongside strong security measures. The research establishes a foundation for improving homomorphic encryption, enhancing key management, and creating a big data security framework specific to photovoltaic energy production.

The widespread availability of digital image processing softwares has given rise to various forms of image manipulation and forgery which can pose a significant challenge in different fields such as law enforcement, journalism etc,. it can also lead a privacy concern. We are proposing a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for purpose of Investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images if the forged portion (dummy image) has a compression quality different from the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10x10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.