In this paper, we compare and analyze light-weight approaches for instantaneous smart
meter (SM) data obfuscation from a group of consumers. In the literature, the common …

This paper proposes a computational statistical method for multivariate confidential
numerical microdata. The method can be employed for recovering some commonly …

In this big data era, an enormous amount of personal and company information can be
easily collected by third parties. Sharing the data with the public and allowing data users to …

Protecting data privacy and mining statistical information from protected data are the
essential issues in big data. Protecting data privacy through noise-multiplied data is one of …

When multiplicative noises are used to perturb a set of original data, the data provider needs
to ensure that the original values are not likely to be learned by data intruders from the noise …

Data privacy is an issue of increasing importance for big data mining, especially for micro-
level data. A popular approach to protecting the such is perturbation. Therefore, techniques …

This paper assesses the usefulness of a proposed multiplicative perturbation method by
contrasting the statistical efficiency achieved in point hypothesis testing of simple …

Perturbing sensitive data is one of the standard ways of protecting confidential data. The
multiplicative noise method is one of these data perturbation methods and this method has …

In this paper, we compare and analyze light-weight approaches for instantaneous smart
meter (SM) data obfuscation from a group of consumers. In the literature, the common …

Smart meter electricity data presents a risk of malicious agents gaining insight of private
information, including residents' lifestyle and daily habits. In this paper, we consider two …