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This results in a masked meter value (tilde m_{i} = m_{i} + s_{i} mod kappa ).
We further assume all devices to be tamper-proof, i.e., the meter value itself cannot be manipulated.
In the following, it is shown that applying the wavelet transform to a meter value and masking can be combined in order to provide a certain resolution only.
The only difference is that instead of a single meter value, share and key, respectively, a (spatially) aggregated sum of values is used.
This is achieved by adding for each SM i at time t a random share s i in the range 1,…,κ−1 to the meter value m i.
As described in [9], for each meter value at time t each SM i creates a random share by s_{i} = sumlimits_{k neq i} (-1)^{b i,j)} H(K_{i,j} mathrm{vert vert} t), (6).
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Or things like scanning electricity meter values, serial numbers and "other process enhancing information".
The aggregator receives an aggregation of the masked meter values sumlimits_{i} tilde m_{i} = sumlimits_{i} left(W left(m_{i} right) + s_{i} right), (21).
Defining for each node j a vector with the corresponding laser distance meter values L′(j)=[L j),…,L j)] T with dimension n×1 (treated here as ground truth), then the mean squared error at each pixel k and for node j can be calculated as MSE k ( j ) = 1 N ′ ∑ i = 1 N ′ ∥ Z i ( j ) − L ′ ( j ) ∥ 2 (11).
This analysis was repeated for paired sensor glucose values and point-of-care meter values in the same alarm settings and target range.
Similarly, the Clinical Laboratory and Standards Institute CLSII C30-A2) sthats that 95% of meter values must be within 20% (or 15 mg/dl for values <75 mg/dl) of a reference method.
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