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)`

- x
object from class freqCalc

- method
approx (default) or exact

- qual
final correction factor

- survey
TRUE, if we have survey data and FALSE if we deal with a population.

rk: base individual risk

method: method

qual: final correction factor

fk: frequency count

knames: 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
```