Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation

Abstract

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 approach is to use additive Gaussian noise based SM data obfuscation. In order to investigate the effects of different approaches, we consider Gaussian, Rayleigh, generalized Gaussian and chi-square distributions to achieve either additive or multiplicative data obfuscation. For each type of obfuscation approach, we calculate the required parameters to achieve obfuscation such that 50% of the obfuscated data fall outside an interval equalling twice the mean of the instantaneous SM measurements. We also calculate the minimum number of SMs required to estimate the mean of the actual SM measurements, such that the estimate varies within only 0.5% of the actual mean with a 99.5% probability. Simulation results are used to verify the calculations, and it is shown that multiplicative Rayleigh and generalized Gaussian noise require the least number of SMs, which is 90% less than the traditional approach of additive Gaussian noise-based SM data obfuscation.

Interaction of Secure Cloud Network and Crowd Computing for Smart City Data Obfuscation

“…Because of this, incremental learning techniques also leverage this mechanism. Thus, AODE has much promise and is utilized as a replacement for a classification approach due to its many attractive qualities [ 25 ].…”

“…Several different sorts of data may be used using this method. By applying anonymity to the data records and mixing the data with the data structure, it is, therefore, simple to keep the data using this technique while simultaneously maintaining a realistic-looking database that is difficult to distinguish from a database made up of masked data [ 25 ].…”

“…If the count of pre-processed data is lesser than the count of the parallel server, the time taken to retrieve the data rate is positively low. Otherwise, we recheck the count of pre-processed data [ 25 ].…”

Gridchain: an investigation of privacy for the future local distribution grid

“…Changing the granularity of the collected data as in [66] or using differential privacy as in [67], mean that a customer is not charged for her actual consumption. It should be noted that limiting the consequences to the utility of noise-adding techniques is an ongoing research challenge (see, e.g., [35]). In this work, we focus on having correct billing but still share data that may be useful for grid operation while making NILM analysis more challenging.…”