Privacy-Enhancing Aggregation Techniques for Smart Grid Communications by Rongxing Lu

Author:Rongxing Lu
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Step-2: Then, U i chooses a random number , and computes

(5.1)

Step-3: Eventually, U i reports C i, γ to the GW through WiFi.

Note that for residential users, the electricity usage within 15 min could not be extremely high, thus choosing an appropriate large W is enough in reality.

5.3.3 Privacy-Preserving Report Aggregation

After receiving all n encrypted usage data C i, γ for i = 1, 2, …, n, the GW could aggregate users’ data based on the CC’s requirements in a privacy-preserving way. Define the aggregation of users’ data as a function which takes users’ data as input and the aggregated result as output. Then our basic scheme can support multiple aggregation functions depending on the CC’s requirements. We illustrate three kinds of aggregations which are used frequently in statistics, i.e. average, variance and one-way ANOVA. Note that our scheme is not limited in the aforementioned aggregations. In fact, for any given aggregation function that has the maximum degree of 2, the GW could aggregate the users’ data in a privacy-preserving way by using our scheme.

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