Steps 4 and 5 then use secure comparInsteadMPARE (Protocol 4) and accumulate the results into where if and only if for some.
As in the basic scheme, all smart meters SM i calculate a random share s i,t for t=0…T.
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.
Each smart meter SM i calculates at each time t=0…T a masked value (tilde m_{i,t}) by adding a random share s i,t to its measured value m i,t.
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).
TTP agrees with all smart meters in the group G={SM1,…,SM N } on providing a resolution r of a total of T values to an aggregator A. Simultaneously, all SM i and TTP calculate a random share s i,t for t=0…T, as described for the principal masking above.
The bottom black line represents the hypothesis of random shared interests and the top grey line is the fit of data on itself - together forming an upper and lower limit for comparing hypothesis.
Validation of pooling was done by visual inspection of 100 random, shared SNPs for overt batch effects; none was observed.
There were hundreds of them, seemingly random, sharing only a macabre tenor: headlines about injuries and death, especially murders and fatal accidents; articles about war casualties, robberies, automobile accidents, and even plane crashes.
After all, if Oprah or Tim Ferriss shared your article with their followers, it will generate much more retweets/likes than if a random person shared it.
