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)`

## Arguments

- 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.

## Value

- rk:
base individual risk

- method:
method

- qual:
final correction factor

- fk:
frequency count

- knames:
colnames of the key variables

## Details

S4 class sdcMicro objects are only supported by function *measure_risk*
that also estimates the individual risk with the same method.

## Note

The base individual risk method was developed by Benedetti,
Capobianchi and Franconi

## References

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.

## Author

Matthias Templ. Bug in method “exact” fixed since version
2.6.5. by Youri Baeyens.

## Examples

```
## 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
```