Estimation of the risk for each observation. After the risk is computed one can use e.g. the function localSuppr() for the protection of values of high risk. Further details can be found at the link given below.
indivRisk(x, method = "approx", qual = 1, survey = TRUE)object from class freqCalc
approx (default) or exact
final correction factor
TRUE, if we have survey data and FALSE if we deal with a population.
base individual risk
method
final correction factor
frequency count
colnames of the key variables
S4 class sdcMicro objects are only supported by function measure_risk that also estimates the individual risk with the same method.
The base individual risk method was developed by Benedetti, Capobianchi and Franconi
Templ, M. and Kowarik, A. and Meindl, B. Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro. Journal of Statistical Software, 67 (4), 1--36, 2015. doi:10.18637/jss.v067.i04
Franconi, L. and Polettini, S. (2004) Individual risk estimation in mu-Argus: a review. Privacy in Statistical Databases, Lecture Notes in Computer Science, 262--272. Springer
Machanavajjhala, A. and Kifer, D. and Gehrke, J. and Venkitasubramaniam, M. (2007) l-Diversity: Privacy Beyond k-Anonymity. ACM Trans. Knowl. Discov. Data, 1(1)
additionally, have a look at the vignettes of sdcMicro for further reading.
## example from Capobianchi, Polettini and Lucarelli:
data(francdat)
f <- freqCalc(francdat, keyVars=c(2,4,5,6),w=8)
f
#>
#> --------------------------
#> 4 obs. violate 2-anonymity
#> 8 obs. violate 3-anonymity
#> --------------------------
f$fk
#> [1] 2 2 2 1 1 1 1 2
f$Fk
#> [1] 110.0 84.5 84.5 17.0 541.0 8.0 5.0 110.0
## individual risk calculation:
indivf <- indivRisk(f)
indivf$rk
#> [1] 0.01714426 0.02204233 0.02204233 0.17707583 0.01165448 0.29706308 0.40235948
#> [8] 0.01714426