Biblio

Image watermarking is very important phenomenon in modern society where intellectual property right of information is necessary. Considering this impending problem, there are many image watermarking methods exist in the literature each of having some key advantages and disadvantages. After summarising state-of-the-art literature survey, an optimum digital watermark technique using singular value decomposition with principle component analysis (PCA) is proposed and verified. Basically, the host image is compressed using PCA which reduces multi-dimensional data to effective low-dimensional information. In this scheme, the watermark is embedded using the discrete wavelet transformation-singular value decomposition approach. Simulation results show that the proposed method improves the system performance compared with the existing method in terms of the watermark embedding, and extraction time. Therefore, this work is valuable for image watermarking in modern life such as tracing copyright infringements and banknote authentication.

This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.