Biblio

In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer's behaviors, and process a batch of data at each time. Firstly, differing from traditional differential private approach-es, we add randomized response noise into the behavior signa-tures matrix to achieve an acceptable utility-privacy tradeoff. Secondly, a behavior signature modeling method based on sparse coding is proposed. After some lightweight trainings us-ing the energy consumption data, the dictionary will be associat-ed with the behavior characteristics of the electric appliances. At last, through the experimental results verification, we find that our Algorithm can preserve consumer's privacy without comprising utility.

In-loop filtering is an important task in video coding, as it refines both the reconstructed signal for display and the pictures used for inter-prediction. In order to remove coding artifacts, machine learning based methods are assumed to be beneficial, as they utilize some prior knowledge on the characteristics of raw images. In this contribution, a dictionary learning / sparse coding based inloop filter and a frequency adaptation model based on the lp-ballenergy in the spectral domain is proposed. Thereby the dictionary is trained on raw data and the algorithms are controlled mainly by the parameter for the sparsity. The frequency adaption model results in further improvement of the sparse coding based loop filter. Experimental results show that the proposed method results in coding gains up to l-4.6 % at peak and -1.74 % on average against HEVC in a Random Access coding configuration.