Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering

Abstract

Abstract-Smart grid users and standardization committees require that utilities and third parties collecting metering data employ techniques for limiting the level of precision of the gathered household measurements to a granularity no finer than what is required for providing the expected service. Data aggregation and data perturbation are two such techniques. This paper provides quantitative means to identify a tradeoff between the aggregation set size, the precision on the aggregated measurements, and the privacy level. This is achieved by formally defining an attack to the privacy of an individual user and calculating how much its success probability is reduced by applying data perturbation. Under the assumption of timecorrelation of the measurements, colored noise can be used to reduce even further the success probability. The tightness of the analytical results is evaluated by comparing them to experimental data.

The Challenges of Privacy and Access Control as Key Perspectives for the Future Electric Smart Grid

“…In the light of eliminating the drawbacks brought by the TTP technique, data aggregation by the use of perturbation was proposed. The perturbation technique focuses on adding noise or some means of randomness to the metering data of each smart meter so that the aggregating node or entity does not infer the metering data [50], [51], [52]. One major drawback of aggregation protocols by means of perturbation is that due to the randomness added to each smart meter, the aggregated data can not reflect exactly the aggregated real metered data.…”

Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

“…However, in 2012, the European Commission recommended keeping a frequency under 15 min to "allow the information to be used to achieve energy savings" [12]. Several works explore the trade-offs between privacy and the operational needs of Smart Grid data mainly by investigating different data resolution schemes and load shaping [2,8,26,42,43], but this research field is still considered to have many open challenges. In fact, the Smart Grid data minimisation is a well-studied case study for the more general problem of time series compression [9].…”

On Privacy-Preserving Protocols for Smart Metering Systems

“…Savi et al [SRV15] presented an analysis on schemes based on noise to quantify a trade-off between the number of measurements that compound the consolidated consumption c j and the precision on c j . Some previous work used differential privacy [Dwo08] to analyze a specific PrivacyPreserving Protocol (PPP), for instance, the work of Jawurek and Kerschbaum [JK12].…”